DATA-BASED SOFT SENSING FOR CHEMICAL REACTOR TO ESTIMATE VARIABLES AND DETECT ABNORMALITIES BACKGROUND [0001] In the fields of chemical and bioprocess engineering, reactors may be used to control a chemical reaction. Chemical reactions include, but are not limited to, polymerization reactions, synthesis reactions, decomposition reactions, biological reactions, and biochemical reactions. The chemical reaction takes place inside the reactor. A reactor system may include a reactor, a control system, and sensors that monitor reaction parameters. The control system receives data from the sensors, compares the data with desired target values, and derives command functions that are used to control the reaction by operation of control components, such as valves and switches. [0002] Process monitoring is an important aspect of the chemical industry to ensure safety and maintain product quality. Abnormality detection and abnormality diagnosis are important facets of the process monitoring. These can involve early detection of process drifts, abnormalities, identifying the abnormal equipment or origins and then taking corrective action by the human operator. Due to the broad scope of detection activity caused by several malfunctions such as equipment failure, process drifts, process degradation, etc., and due to the complexity and size of the industrial operations, it is difficult to completely rely on human operators to cope with process monitoring issues. Hence, there is a need to develop models and algorithms to detect and diagnose abnormalities in plants so that a safe and efficient operation can be ensured. [0003] Model-based abnormality detection and diagnosis methods use mathematical models of the supervised systems. Model-based methods require two steps; in the first step, a residual is generated by comparing actual and expected data. In the second step, diagnosis is performed by using a decision rule. Industrial processes are very complex in nature, adding increased difficulty when developing accurate mathematical models describing the processes. Thus, some of the model-based approaches are limited to linear and some specific nonlinear system models. These approaches are not based on general nonlinear models that use a linear approximation, as they might reduce the abnormality detection performance depending on the efficiency of the linearization. SUMMARY [0004] This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter. [0005] In one aspect, embodiments disclosed herein relate to a system for producing a chemical product, where the system includes a reaction zone and an information processing device, where the reaction zone is under a plurality of monitored conditions and is configured to produce the product from reactants using a chemical process characterized by a plurality of variables, and where the information processing device includes a computer processor configured to estimate the variables from the monitored conditions and detect one or more abnormalities in the variables. [0006] In another aspect, embodiments disclosed herein relate to a method for producing a chemical product, where the method includes implementing a chemical process for producing the product from the reactants in a reaction zone under a plurality of monitored conditions, the chemical process characterized by a plurality of variables; estimating the variables from the monitored conditions with an information processing device; and detecting one or more abnormalities in the variables with the information processing device. [0007] Other aspects and advantages of the claimed subject matter will be apparent from the following description and the appended claims. BRIEF DESCRIPTION OF DRAWINGS [0008] FIG.1 shows a system, including a reaction zone, with said zone including a reactor and a processing device, with said device including a computer processor in accordance with one or more embodiments. [0009] FIG.2 shows a workflow in accordance with one or more embodiments. [0010] FIG.3 shows a reactor in accordance with one or more embodiments. [0011] FIG.4 shows a diagnostic process in accordance with one or more embodiments. [0012] FIG. 5 shows another diagnostic process in accordance with one or more [0013] FIG.6 shows a GUI in accordance with one or more embodiments. [0014] FIG.7 shows a computer system in accordance with one or more embodiments. [0015] FIGS. 8A and 8B show exemplary results of estimated variables for respective batches of a chemical process in accordance with one or more embodiments. [0016] FIGS.9A and 9B show exemplary abnormality detection and subsequent diagnosis in accordance with one or more embodiments. DETAILED DESCRIPTION [0017] In the following detailed description of embodiments of the disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the disclosure may be practiced without these specific details. In other instances, well- known features have not been described in detail to avoid unnecessarily complicating the description. [0018] Throughout the application, ordinal numbers (e.g., first, second, third, etc.) may be used as an adjective for an element (i.e., any noun in the application). The use of ordinal numbers is not to imply or create any particular ordering of the elements nor to limit any element to being only a single element unless expressly disclosed, such as using the terms "before", "after", "single", and other such terminology. Rather, the use of ordinal numbers is to distinguish between the elements. By way of an example, a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements. [0019] The present disclosure provides data-based methods and systems for chemical process monitoring that allow abnormality detection and diagnosis in the chemical process. [0020] One or more embodiments relate to a system for producing a chemical product, the system including a reaction zone and an information processing device, where the reaction zone is under a plurality of monitored conditions and is configured to produce the product from reactants using a chemical process characterized by a plurality of variables, and where the information processing device includes a computer processor configured to estimate the variables from the monitored conditions and detect one or more abnormalities in the variables. [0021] FIG.1 shows a system 100 according to one or more embodiments. In one or more embodiments, the system 100 of FIG.1 may be employed in any production facility, such as a chemical plant, to make a chemical product. The system 100 is suitable for a chemical process, such as a chemical process to produce a chemical product. The system 100 includes a reaction zone 110 and an information processing device 210 coupled to the reaction zone 110. System 100 may include one or more feed and product streams. Each feed stream contains one or more reactants, is coupled to the reaction zone 110 and is introduced into the system 100. Each product stream contains the chemical product, is coupled to the reaction zone 110, and passes from the system 100. Each feed stream can include one or more of a gas, a liquid, and a solid and combinations thereof. Each product stream can include one or more of a gas, a liquid, and a solid and combinations thereof. Each feed stream can couple to the reaction zone 110 at a side, the top, or the bottom of a reactor, for example at a location according to the composition of the feed stream. Each product stream can couple to the reaction zone 110 at a side, the top, or the bottom of a reactor, for example at a location according to the composition of the product stream 116. An optional effluent stream can couple to the reaction zone 110 and pass from the system 100. The effluent stream can include one or more of a liquid, and a solid and combinations thereof. An optional off-gas stream can couple to the reaction zone 110 and pass from the system 100. The effluent stream or off-gas stream can couple to the reaction zone 110 at a side, the top, or the bottom of a reactor, for example at a location according to the composition of the effluent stream. FIG. 1 shows system 100 in an exemplary configuration with gas feed stream 114, off-gas stream 116, solid/liquid feed stream 118, chemical product stream 120, and effluent stream 122. Reaction zone 110 can include one or more reactors. One or more of the reactors each independently can include a heat exchanger (not shown), such as a water jacket around the reactor configured to receive and pass liquid for heating or cooling. FIG.1 shows system 100 in a configuration with a reactor 130 defining reaction zone 110. [0022] In one or more embodiments, the information processing device is operatively connected to the reaction zone. In one or more embodiments, the computer processor is configured to estimate the variables from monitored conditions and detect one or more abnormalities in the variables in real time during the chemical process. [0023] In one or more embodiments, the system includes a plurality of sensors coupled to the reaction zone and in communication with the information processing device, where each sensor is configured to measure at least one of the conditions. [0024] FIG.1 shows an information processing device 210 in system 100 and one or more sensors, including one or more probes. The probes may be configured to monitor certain conditions. FIG.1 shows a first probe 214 and a second probe 216 that are coupled to the reactor chamber 130. The sensor may be situated inside the reactor chamber, coupled to the reactor chamber (shown in FIG.1), or outside the reactor chamber, depending on the function of the sensor. Other sensors may include, for example, a flow rate sensor coupled to a solid/liquid feed stream (such as 118 in FIG.1) that is configured to monitor a flow rate. The one or more sensors may be in signal communication with the information processing device 210. The signal communication may be a wired communication, a wireless communication, or a combination thereof. [0025] A chemical process takes place within the reaction zone. Types of chemical processes include, but are not limited to, a catalytic process, an enzymatic process, a stoichiometric process (such as non-catalyzed), a polymerization process, a fermentation process (a “bio-fermentation” process), a biomass production process, and a combination thereof. [0026] The reactor uses materials during the chemical process to produce the chemical product. Thus, the reaction zone is configured to retain materials for the chemical process. A material for the chemical process can be selected from the group consisting of a catalyst, a reactant, an enzyme, an enzyme-substrate, a monomer, an activator, a microorganism, a biomass, a food source, a gas, a solvent, and combinations thereof. When the chemical process is polymerization, a reactant can be a monomer. One or more materials are used during the chemical process. A material, such as a reactant, may be consumed in the chemical process. Alternately, a material, such as a catalyst, may facilitate the chemical process without being consumed by the process. The materials are introduced into the reaction zone via the one or more feed streams. The term “food source” relates to a carbon-based material for the growth of microorganisms, for example, a starch or sugar such as mannose, sorbitol, glucose, fructose, or lactose. The term enzyme-substrate is used to differentiate from the term substrate. A “substrate” is a material to be used in a reactor. An “enzyme-substrate” is a molecule upon which an enzyme acts. Thus, an enzyme-substrate may be a substrate. Materials for use in chemical processes and reactors are well appreciated in the art. [0027] In one or more embodiments, the reaction zone includes one or more chemical reactors. Suitable examples of reactors include, but are not limited to, a tubular reactor, a bubble column reactor, a bubble cap reactor, a plug flow reactor, an airlift reactor, a two-stage airlift reactor, a fluidized bed reactor, a stirred tank reactor, a Heinze-type reactor, a fixed bed reactor, a packed bed reactor, a stainless steel fixed bed reactor (for example, with electric heater), a microwave reactor, and a photoreactor. In one or more embodiments, the reactor is a bioreactor. When the reactor is a bioreactor, it may be a fermenter, such as a batch fermenter, a continuous fermenter, or a recycle reactor. [0028] In addition to reactor components previously described, each reactor can independently include an agitator, a sparger, an antifoam stream, an acid stream, a base stream, or a combination thereof. [0029] When the feed stream includes a gas, the gas can include a suitable gas for the chemical process. In some instances, the gas is one or more of air, oxygen, nitrogen, argon, helium, carbon monoxide, carbon dioxide, hydrogen, ozone, ethylene, and combinations thereof. In some instances, the gas is a gaseous reactant. In some instances, the gas is inert, such as nitrogen, argon, helium, or other suitable noble gas. In some instances, the gas is a growth agent of a food source, for example, in a bioreactor, such as oxygen, carbon monoxide, or carbon dioxide. When a microorganism is included in a reactor, the gas used may relate to the aerobic or anaerobic respiration of said microorganism. [0030] When the feed stream includes a liquid, the liquid can include a suitable liquid for the chemical process. In some instances, the liquid is a liquid reactant. In some instances, the liquid includes a solvent. The solvent can include acetone, acetic acid, ethyl acetate, pentane, hexane, heptane, dichloromethane, chloroform, methanol, ethanol, isopropanol, tetrahydrofuran, acetonitrile, dimethylformamide, toluene, dimethylsulfoxide, water, or a combination thereof. In some instances, the liquid contains bubbles of a gas. [0031] When the feed stream includes a solid, the solid can include a suitable solid for the chemical process. In some instances, the solid is a reactant. In some instances, the solid is a catalyst. In some instances, the solid is dispersed in a liquid solvent. In some instances, the solid is suspended in a gas. [0032] The optional off-gas stream includes an off-gas. The off-gas is a gas that is expelled from the reactor. Thus, it is a type of gas product stream that may be collected, further processed, or re-routed to the reactor. The off-gas may be a gas that is introduced into the reactor and is expelled, or a byproduct or product of a chemical process. For example, a byproduct or a chemical product off-gas may include, but is not limited to, hydrocarbons such as ethylene, carbon dioxide, hydrogen, or oxygen. [0033] The optional effluent stream includes an effluent. The effluent is a liquid, a solid, or a liquid and a solid that is expelled from the reactor. The effluent may be a liquid or a solid that is introduced into the reactor and is expelled, or a byproduct or product of a chemical process. For example, an effluent may include, but is not limited to, solvent, biomass, or intractable reaction material. Various other examples of effluents from a chemical process would be appreciated by one of ordinary skill in the art. [0034] The chemical product stream includes a chemical product of the chemical process. Various chemical products from a chemical process in a reaction zone are appreciated in the art. A chemical product from a reaction zone may include a polymer, a hydrocarbon, a synthetic natural product, or various other chemical products. As a non-limiting example, a polymer may include latex, acrylic, silicone, polyurethane, natural rubber, synthetic rubber, polyethylene, polypropylene, polybutene, polyisobutylene, polymethylpentene, ethylene propylene rubber, ethylene propylene diene monomer rubber, and combinations thereof. The polymer is not particularly limited. The polymer may be selected from the group consisting of polyvinyl alcohol, polyvinylpyrrolidone, celluloses (such as carboxymethylcellulose, hydroxyethylcellulose, hydroxypropylcellulose and the like), polyacrylamide, polyethylene oxide, polyethylene glycol, polypropylene glycol, copolymers of propylene oxide and ethylene oxide, polyvinyl acetal, polyvinyl methyl ether, polyamine, polyethyleneimine, casein, gelatin, starch, polyolefins (such as polyethylene, polypropylene, and copolymer resins with other olefinic monomers), polyesters (such as polycaprolactone), polyvinyl chloride resins, polystyrenes (such as polystyrene, acrylonitrile-styrene copolymer resin and the like), acrylates (such as polymethyl methacrylate, copolymers of (meth)acrylate, acrylonitrile-methyl acrylate copolymer and the like), polycarbonates, polyurethanes, vinyl chloride-vinyl acetate copolymer, polyvinylbutyral and the like; polyisobutylene, polytetrahydrofuran, polyaniline, acrylonitrile-butadiene-styrene copolymer (ABS resin), polyamides, polyimides, polydienes (such as polyisoprene, polybutadiene and the like), polysiloxanes (such as polydimethylsiloxane and the like), polysulfones, polyimines, polycarboxylic acid anhydrides, polyureas, polysulfides, polyphosphazenes, polyketones, polyphenylenes, polyhaloolefins, and derivatives thereof. As a non-limiting example, a hydrocarbon may selected from the group consisting of xylene, toluene, benzene, ethylbenzene, poly alpha-olefins, and combinations thereof. [0035] In one or more embodiments, the chemical product is a polymer, and the reaction zone includes a polymerization reactor. [0036] Polymer reactors can be vessels in which the chemicals react under controlled conditions to create the desired products. Agitators in the reactors can keep the chemicals well mixed while the reaction progresses. The temperature in the polymer reactor can be maintained by cooling/heating jackets and a baffle in the reactor. The major raw materials and sub-raw materials are fed into a reactor. [0037] When the chemical product is a polymer, in one or more embodiments, the product is latex. Latex is typically produced as a liquid product. Liquid latex is produced in the Polymerization reactor by reacting monomers, emulsifiers, catalysts, stabilizers, crosslinkers, and hot demineralized water. Demineralized water (“PW”) is generally the first material charged to the reactors to aid in component blending and emulsifier dispersal. Monomers are the basic chemicals for latex production and form the polymer chain during the reaction. Emulsifiers are added to control the size of the latex particles, and catalysts help initiate the reaction. Stabilizers help reduce scale formation and help control the polymerization reaction. Crosslinkers strengthen the polymer chains. [0038] In one or more embodiments, the monitored conditions are selected from the group consisting of pH, temperature, fluid level, pressure, other monitored conditions, and combinations thereof. [0039] In one or more embodiments, the variables include reaction variables selected from the group consisting of a conversion of one of the reactants; a solid content in the reaction zone; a heat removed from the reaction zone; a heat accumulated in the reaction zone; and other variables and combinations thereof. [0040] In one or more embodiments, the computer processor is configured to estimate the variables from the monitored conditions by applying one or more of mass balance and energy balance to the chemical process. [0041] In one or more embodiments, the computer processor is configured to detect abnormalities in the variables by determining deviations of the variables from historical values within thresholds. [0042] In one or more embodiments, the reaction zone is characterized by diagnostic conditions, and the computer processor is configured to identify one or more of the diagnostic conditions as a source of the one or more abnormalities. In one or more embodiments, the computer processor is configured to identify one or more diagnostic conditions as a source of the one or more abnormalities by comparing the diagnostic conditions with historical values having predetermined negative impact on the chemical process. [0043] The above-described features can be used singly or in combination. Thus, for example, a system for producing a chemical product can include a polymerization reactor with a plurality of sensors coupled to the polymerization reactor, and an information processing device operatively connected to the polymerization reactor and in communication with the plurality of sensors, where the polymerization reactor is under a plurality of monitored conditions, where the polymerization reactor is configured to produce the polymer from a monomer using a chemical process characterized by a plurality of variables, with said variables comprising reaction variables selected from the group consisting of a conversion of the monomer; a solid content in the polymerization reactor; a heat removed from the polymerization reactor; a heat accumulated in the polymerization reactor; and combinations thereof; and where each sensor is configured to measure at least one of the conditions; and wherein the information processing device comprises a computer processor configured to, during the chemical process (that is in “real” time, i.e. in the same time frame as the chemical process): estimate the variables from the monitored conditions by applying one or more of mass balance and energy balance to the chemical process; detect abnormalities in the variables by determining deviations of the variables from historical values within thresholds; and identify one or more diagnostic conditions as a source of the one or more abnormalities by comparing the diagnostic conditions with historical values having predetermined negative impact on the chemical process. [0044] One or more embodiments relate to a method for producing a chemical product, the method including: implementing a chemical process for producing the product from the reactants in a reaction zone under a plurality of monitored conditions, the chemical process characterized by a plurality of variables; estimating the variables from the monitored conditions with an information processing device; and detecting one or more abnormalities in the variables with the information processing device. [0045] In one or more embodiments, the information processing device is operatively connected to the reaction zone, where the estimating and detecting occur in real time during the implementing. [0046] In one or more embodiments, the information processing device is in communication with a plurality of sensors coupled to the reaction zone, where the method further comprises measuring the conditions with the sensors. [0047] In one or more embodiments, the estimating includes applying one or more of mass balance and energy balance to the chemical process. [0048] In one or more embodiments, the detecting includes determining deviations of the variables from historical values within thresholds. [0049] In one or more embodiments, the reaction zone is characterized by diagnostic conditions, and the method further includes identifying one or more of the diagnostic conditions as a source of the one or more abnormalities with the information processing device. In one or more embodiments, the identifying includes comparing the diagnostic conditions with historical values having predetermined negative impact on the chemical process. [0050] In one or more embodiments, the monitored conditions are selected from the group consisting of pH, temperature, fluid level pressure, other monitored conditions, and combinations thereof. [0051] In one or more embodiments, the variables are selected from the group consisting of a conversion of one of the reactants, a solid content in the reaction zone, a heat removed from the reaction zone, a heat accumulated in the reaction zone, other variables, and combinations thereof. [0052] In one or more embodiments, the chemical product is a polymer, and the reaction zone comprises a polymerization reactor. [0053] The above-described features can be used singly or in combination. [0054] As an illustration of the present system and process, FIG.2 shows an embodiment of a data-based method for chemical process monitoring that allows abnormality detection and diagnosis in the chemical process. FIG.2 shows a data-based workflow in accordance with one or more embodiments. Specifically, FIG. 2 describes a general method that uses historical data to detect one or more abnormalities in the variables with the information processing device. One or more blocks in FIG.2 may be performed by one or more components (e.g., information processing device 210, FIG.1) as described in FIG. 1. While the various blocks in FIG.2 are presented and described sequentially, one of ordinary skill in the art will appreciate that some or all of the blocks may be executed in different orders, the blocks may be combined or omitted, and some or all of the blocks may be executed in parallel. Furthermore, the blocks may be performed actively or passively. [0055] In block 200, sensor and/or charge conditions associated with a reactor are obtained. The reaction is in the reaction zone. The sensor values are received, for example, from monitored reactor and jacket temperatures and flow sensors, as well as from material charging data. [0056] FIG.3 shows a reactor with three jackets according to one or more embodiments. It will be understood that other configurations may be used. A resin reactor 300 includes a first jacket 302, a second jacket 304, a third jacket 306, and an agitator 308. The jackets are heat exchangers. The jackets each are configured for input and exit coolant streams for passing water as coolant through the jacket to provide heat exchange. Cooling water (“CW”) 310 and low pressure steam (“LS”) 312 enter the first jacket 302 and cooling water return (“CWR”) 314 exits the first jacket 302. Refrigerated water (“RW”) 316 enters the second jacket 304 and refrigerated water return (“RWR”) 318 exits the second jacket 304. Refrigerated water (“RW”) 320 enters the third jacket 306 and refrigerated water return (“RWR”) 322 exits the third jacket 306. Resin reactor 300 may be utilized in the workflow of FIG.2 for example at block 200. [0057] In block 220, historical batches associated with the chemical process are obtained. The historical batches can contain a large amount of data. [0058] In block 230, representative trends in variables can be generated from the historical batches. The variables can be, for example, conversion, solid content, and heat removal from individual jackets, (for example, the first jacket and the second jacket of FIG. 3) and total heat removal. Heat removal for an individual jacket (for example the third jacket of FIG.3) can be determined from the total heat removal and the heat removals from the other individual jackets. The representative trends can be determined for values at each point in time when the variable is evaluated. [0059] In block 240, “soft sensor” estimated variables are computed from the sensor and/or charge values for a new test batch associated with the chemical process. The soft sensor estimated values can be, for example for conversion, solid content, and heat removal charts. The soft sensor estimation of variables is described more fully below. [0060] In block 250, an abnormality is detected when there is a significant difference between the estimated values and the past golden batch values. A golden normal batch is a batch or average of multiple batches that represents the normal behavior of a system. The comparisons to evaluate for a variable can be made at each point in time when the variable is evaluated. The time increments can be the same for the estimated values and the past batch values. At a given time, the estimated values can be within or outside a threshold relative to the past batch values. That is when the difference between the estimated value and the past batch values exceeds the threshold, an abnormality is detected. [0061] In block 260, a likely candidate for the abnormality source is diagnosed. The source can be a diagnostic condition. Thus, the source of the soft sensor abnormality can be determined by diagnosing one or more source condition abnormalities. Diagnosis of diagnostic conditions is described more fully below. [0062] Estimating monomer conversion in a batch polymerization process with the information processing device using heat balance may be carried out as follows according to one or more embodiments. The estimation technique is termed herein a soft sensor technique. This technique may be utilized in the workflow of FIG.2 at block 240. [0063] A heat balance algorithm can be utilized to develop a soft sensor for estimating conversion in the polymer reactor. When the polymer reaction is an exothermic reaction, for example for in latex production, jackets and a baffle can be utilized to cool the reactor and maintain the temperature at a set point. During the heating cycle prior to the start of the polymer reaction, the jackets can be used to increase the temperature inside the polymer reactor. Then throughout the reaction, the reactor jackets extract the exothermic heat from the polymer reactor in order to keep the temperature at a predetermined level. [0064] To develop a soft sensor for estimating conversion of the monomer to polymer, the conservation of energy law can be applied to a polymerization reactor. In one or more embodiments, the rate of heat accumulation in the reactor equals the rate of heat generation minus the rate of heat flow from the surroundings of the reactor plus the rate of energy added to the reactor by mass flowrate minus the rate of energy removed from the reactor by mass flowrate minus the heat loss. The heat loss can be from radiation and/or conduction. [0065] In a batch process, the rate of energy removed by mass flowrate is zero. Heat loss tends to be insignificant in the examples described and is assumed to be zero below. However, it will be understood by one of ordinary skill in the art that heat loss can be incorporated using equations known to one of ordinary skill in the art. In one or more embodiments, the individual terms in heat balance further simplify to rate of heat flow from the surroundings equals heat transfer through the reactor jackets and rate of energy added by mass flowrate equals heat transfer through materials addition, rate of heat accumulation equals heat of accumulation in the reactor, and rate of heat of generation equals conversion of the monomer multiplied by heat of reaction. [0066] The heat transfer through a reactor jacket (“HT _jacket ”) is ( ¹⁾ where (lb/sec) and (cal/lb/°C) are the mass flow rate and specific heat of the cooling or heating fluid in the Jacket, ^ _^^^ (°C) is the outlet jacket t ^{emperature, and (°C) is the inlet jacket temperature.} [0067] The heat transfer through addition of a raw material (“HTraw”) is (2) w ^here, ^ ^{(lb/sec) and} (cal/lb/°C) is the amount and specific heat of t ^ he c ^{harged raw material. (°C) is the temperature of the charged raw material,} ( ^{°C) and is the reactor temperature.} 13 [0068] The heat of accumulation in the reactor (“HA _reactor ”) is (3) [0069] ^{where (lb/sec) and} (cal/lb/°C) are the amount and s ^{pecific heat of the mixture in the reactor, (°C) is the reactor temperature at time i+1, and ) and is the reactor temperature at time i.} _[0070] The heat of reaction is (4) w ^{here (cal/lb) is the heat of reaction of the monomer, and} ^ _^^^^^^^ ^(lb/sec) is the amount of monomer charged. [0071] The resulting heat balance equation is ^{[0072] where is the conversion of the monomer.} [0073] Combining equations (1)-(5) gives equation (6): [0074] Equation (6) can be utilized to estimate the conversion of the monomer in the polymer reactor. The heat balance based soft sensor can allow for accurate real-time estimation of conversion utilizing all the available sensor and charging data of the polymer reactor. [0075] Detecting abnormalities may be carried out as follows, according to one or more embodiments. The estimation technique is termed herein a soft sensor technique. This technique may be utilized in the workflow of FIG. 2 at block 250. [0076] In one or more embodiments, the reaction variables are dependent on the monitored conditions, and the reaction variables are estimated as a function of the monitored conditions. The abnormalities can be detected by obtaining values of the estimated variables at time points during reactor operation and comparing the estimated values to reference values at the time points. When an estimated value deviates by +- a threshold number or more from the reference value, this can be characterized as an abnormality. In this way the estimated variables can serve as an alarm to signal proceeding to further diagnosis. The threshold can be set by a level of significance. The threshold can be 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12, %, 13%, 14%, or 15% for example. The threshold can vary with the different estimated variables. That is the threshold for each estimated variable can be independently selected. The threshold for an estimated variable can be determined by the estimated variable’s variation from normal to abnormal behavior. [0077] In one or more embodiments, the data-driven model is generated with a prior knowledge of historical data of the abnormalities that have occurred in the reactor. A time of abnormality can be determined and detected by comparing soft sensor results and the heat removal values with a golden normal batch (as noted above, a batch or average of multiple batches that represents the normal behavior of a system). [0078] In one or more embodiments, the variables are estimated as a function of time. The process of computing the estimated value denoises and smooths from the independent variables. In one or more embodiments, an abnormality is detected as a significant difference (threshold up or down) between the estimated and historical values at a particular time (single value per variable per time). A single threshold reference value for each variable and time can be used rather than multivariate statistics because batch to batch variation is too high, leading to too many false alarms. [0079] In one or more embodiments, the estimated variables are used for an on/off alarm. When the alarm is on, diagnosis proceeds to next step of diagnosis. There may not be a direct formula or relationship between most of the diagnostic variables and the monitored variables. The relevance of the diagnostic variables examined during diagnosis can be based on expert know how. The diagnostic reactor conditions can be analyzed for abnormalities in them. [0080] In one or more embodiments, once an abnormality and time of abnormality is detected, a detailed abnormality diagnosis procedure is carried out. The abnormality diagnosis procedure can be used to detect and identify the abnormality occurring in the chemical/ polymer reactor. [0081] Once the abnormality has been detected, the source of the abnormality is diagnosed, according to one or more embodiments. The abnormality categories and checks can be created using historical process knowledge as well as expert knowledge. The categories of abnormality diagnosis and their checks can be selected from those described below. This technique may be utilized in the workflow of FIG.2 at block 260. [0082] In one or more embodiments, a category of abnormality diagnosis involves observation of the cooling system. In this diagnosis, the inlet temperatures and flow sensor values are compared to a normal batch trend to identify the abnormality. This abnormality diagnosis technique, for example, will direct the engineer to examine the cooler system if the refrigerated water (“RW”) inlet temperature to the Jackets is above 8° C (typical RW temperature range is 4° C to 6° C). Referring to FIG.3, this abnormality diagnosis technique may include checking the temperature of cooling water 310, refrigerated water 316, and/or refrigerated water 318; checking the flow of cooling water 310, refrigerated water 316, and/or refrigerated water 318; and/or checking the recirculation flow of the first jacket 302, the second jacket 304, and/or the third jacket 306. Observation of the cooling system can be used for abnormality diagnosis when, for example, a source of reactor overheating is sought. [0083] In one or more embodiments, a category of abnormality diagnosis involves observation of sensors. Since the reactor temperature sensor is important for the reactor's cooling and heating control, temperature sensor abnormalities can be closely monitored. To check for sensor abnormalities, the temperature and pressure correlation can be monitored and compared to a golden normal batch (as noted above, a batch or average of multiple batches that represents the normal behavior of a system). During polymerization reaction, the temperature and pressure ratio can be measured at hourly intervals. Multiple normal batches can be analyzed to determine the golden batch. Observation of sensors can be used for abnormality diagnosis when, for example, a source of slow reaction is sought. For example, the correlation of temperature with pressure can be used to check if a temperature sensor is faulty, by comparison with monitored pressure. [0084] In one or more embodiments, a category of abnormality diagnosis involves observation of monomer charge. The level of a monomer separator tank can be measured and compared before and after the monomer charge. This check ensures that the inhibitor has not been charged with monomer charge to the poly reactor. In normal operation, the monomer separator tank level returns to the same level as before charging when the monomer charge is done. Otherwise, the charged monomer to reactor has an inhibitor. If there is a difference of more than 0.1% between before and after charging, there may be an inhibitor in the charged monomer. Observation of monomer charge fault can be used for abnormality diagnosis when, for example, a source of slow reaction is sought. For example, a monomer inhibitor separator tank level % can be monitored and used to diagnose abnormality. [0085] In one or more embodiments, a category of abnormality diagnosis involves observation of inhibitor leakage. To guarantee that no inhibitor is leaked into a batch, the inhibitor pot level is monitored. During a batch, the inhibitor pot level must be close to constant. As a result, changes in the inhibitor tank level will be tracked throughout a batch. If there is a 1% variation, the inhibitor tank can be regularly monitored; if the variation is 2% to 4% or above, there can be a severe problem with the inhibitor tank. Observation of inhibitor leaking can be used for abnormality diagnosis when for example a source of slow reaction is sought. For example, an inhibitor pot tank level % can be monitored. [0086] In one or more embodiments, a category of abnormality diagnosis involves observation of charging amount. A leaking header valve can result in a lower catalyst charge and, as a result, a slower reaction. Analyzing all the reactor batches can only provide a partial diagnosis of this issue. If the one reactor overheats due to the addition of more catalyst, other reactor on the same raw material charge header line will react slowly due to the low catalyst charge. To detect this problem, all the reactors on the same header line can be monitored at the same time. Observation of charging amount can be used for abnormality diagnosis when, for example, a source of reactor overheating is sought, or when, for example, a source of slow reaction is sought. For example, header valve leakage can cause additional charging and thus overheating. Alternatively, header valve leakage can cause less charging and thus slow reaction. [0087] In one or more embodiments, a category of abnormality diagnosis involves observation of another potential source of abnormality. By assessing the available sensor data, the abnormality diagnosis procedure is aimed to narrow down likely sources of abnormality. If all sensor-based diagnosable abnormalities are ruled out by an elimination process, the diagnosis technique can alert the engineer to look for alternative non- measured abnormality causes, such as raw material concentration, header valve leakage, U value, and so on. As used herein U value is the overall heat transfer coefficient. More specifically U value is defined herein as the overall heat transfer coefficient through the Jackets and reactor. Header valve leakage can be partially diagnosed. Observation of another potential source of abnormality can be used for abnormality diagnosis when, for example, a source of reactor overheating is sought. Observation of another potential source of abnormality can be used for abnormality diagnosis when for example a source of slow reaction is sought. For example, observation of concentration of charged material can be observed, where the charged material can be a catalyst, and/or low sensors can be checked when a source of slow reaction is sought. [0088] In one or more embodiments, the abnormality diagnosis technique can look for abnormality categories that are independent of other sensors and can be identified swiftly. For example, some of the first abnormality criteria that can be diagnosed are as follows: analyze monomer charge abnormality (e.g., check monomer inhibitor separator), analyze inhibitor leakage (e.g., check inhibitor pot level), analyze sensor abnormality (e.g., check temperature sensor). [0089] FIGS.4 and 5 illustrate respective procedures for abnormality diagnosis involving observation of the cooling system, as well as anomaly diagnostic charts for the analysis of the cooling system with the first jacket and second/third jackets. The workflows depict the chronological order for examining the anomalous criteria in one or more embodiments. The workflow streamlines the abnormality checks and fixes the problem of identifying multiple abnormalities. Once the abnormality is detected and a source of abnormality is identified as shown in the abnormality diagnosis procedure, an engineer can take corrective measures to ensure that the polymer reactor is returned to safe operation with little to no loss of productivity. The workflows of FIGS. 4 and 5 can be used singly or in combination. [0090] FIG.4 describes a diagnostic process. This diagnostic process applies for analysis of the cooling system for abnormality of first jacket heat. In block 400, jacket flow and temperature sensor measurements are used to analyze the cooling system. In block 410, an abnormality is detected when the first jacket heat removal exceeds a threshold. The process proceeds to blocks 420 and 430. In block 420, jacket flowmeter values are checked against jacket flowmeter thresholds. In block 440, if a jacket flowmeter threshold is exceeded in block 420, the jacket pattern is checked for abnormality. The jacket pattern has the information of what heat transfer fluid (e.g. cooling water, return water, low pressure steam, or no flow) is flowing through each individual jacket. In block 450, if no jacket pattern issue is found in block 440, the process reports flowmeter sensor failure or low/high flowrate. In block 430, the first jacket inlet temperature is checked. From block 430, the process proceeds to blocks 460 and 470. In block 460, the jacket pattern is checked for any abnormality. In block 480, if no jacket pattern issue is found in block 460, the process reports low/high temperature. In block 470, the delta (that is, change of) temperature of inlet and outlet during heating and cooling cycle is checked. In block 490, if the delta temperature exceeds a temperature change threshold in block 470, the process reports inlet or outlet sensor failure. [0091] FIG. 5 describes another diagnostic process. This diagnostic process applies for analysis of the cooling system for abnormality of second/third jacket heat. In block 500, jacket flow and temperature sensor measurements are used to analyze the cooling system. In block 510, an abnormality is detected when the second or third jacket heat removal exceeds a threshold. As noted above, due to heat balance either the second or third jacket heat can be determined. The process proceeds to blocks 520 and 530. In block 520, jacket flowmeter values are checked against jacket flowmeter thresholds. In block 540, if a jacket flowmeter threshold is exceeded in block 520, the jacket pattern is checked for abnormality. In block 550, if no jacket pattern issue is found in block 540, the process reports flowmeter sensor failure or low/high flowrate. In block 530, the RW inlet temperature is checked. From block 530, the process proceeds to blocks 560 and 570. In block 560, it is determined if the RW inlet temperature is higher than the first threshold (also termed level in FIG.5) or the second threshold (also termed level in FIG.5). The first threshold indicates an alarm of a high level of temperature giving a first warning and the second threshold indicates an alarm of a higher level of temperature than the first threshold, giving a second and final warning. In block 580, if the condition of block 560 applies, the process reports a cooling tower RW check error. In block 570, the delta (that is, change of) temperature of inlet and outlet during heating and cooling cycle is checked. In block 590, if the delta temperature exceeds a temperature change threshold in block 570, the process reports inlet or outlet sensor failure. [0092] It will be understood that it is within the skill of one of ordinary skill in the art to select a procedure for other categories of abnormality diagnosis described herein. [0093] FIG.6 shows a graphical user interface (GUI)-based dashboard 600 that can aid in the application of the soft sensor and abnormality diagnosis techniques described above to a reactor, such as the for the polymerization reactor shown in FIG. 3 and the data- based workflow shown in FIG.2. The dashboard can allow for online and offline modes of soft sensing and abnormality diagnosis, permitting use of its capabilities to monitor process(es) in real time with available data. FIG. 6 shows the graphical user interface (GUI)-based dashboard 600 displaying soft sensor estimation 602, deviation of the soft sensor from a normal batch (i.e. representative historical trend) 604, abnormality diagnosis 606, energy balance 608, heat balance 610, monomer charging 612, and other charging 614. In one or more embodiments, the GUI-based dashboard includes two regions (areas) of displayed data. The GUI-based dashboard 600 includes: a region 616 displaying a soft sensor estimation (in comparison to normal batch trends), abnormality diagnosis (left side of the GUI), and a region 618 displaying energy, heat balance, and charging information (right side of the GUI). The dashboard can be generalized and has the advantage of easy use for engineers and operators. The dashboard design and applicability options can be selected from the process control interfaces currently available in the process industries. [0094] Those skilled in the art will appreciate that while the GUI of FIG.6 is shown with certain proportions of the areas displayed, plots, icons, and specific concentration information for substrates, this GUI is for purposes of example only, and embodiments disclosed herein are not limited to this specific configuration of the GUI or the substrates and concentrations shown. [0095] Embodiments disclosed herein may be implemented on any suitable computing device. Specifically, the information processing device 210 may be any suitable computing device capable of processing data and estimating the variables characterizing the chemical process implemented in the reaction zone from the monitored conditions of the reaction zone and detecting one or more abnormalities in the variables. FIG.7 shows an example computing device that may be implemented as information processing device 210. Specifically, FIG. 7 is a block diagram of a computer system 902 used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures as described in the instant disclosure, according to an implementation. [0096] The illustrated computer 902 is intended to encompass any computing device, such as a high performance computing (HPC) device, a server, desktop computer, laptop/notebook computer, wireless data port, smart phone, personal data assistant (PDA), tablet computing device, one or more processors within these devices, or any other suitable processing device, including both physical or virtual instances (or both) of the computing device. Additionally, the computer 902 may include a computer that includes an input device, such as a keypad, keyboard, touch screen, or other device that can accept user information, and an output device that conveys information associated with the operation of the computer 902, including digital data, visual, or audio information (or a combination of information), or a GUI. [0097] The computer 902 can serve in a role as a client, network component, a server, a database or other persistency, or any other component (or a combination of roles) of a computer system for performing the subject matter described in the instant disclosure. The illustrated computer 902 is communicably coupled with a network 930. In some implementations, one or more components of the computer 902 may be configured to operate within environments, including cloud-computing-based, local, global, or other environment (or a combination of environments). [0098] At a high level, the computer 902 is an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the described subject matter. According to some implementations, the computer 902 may also include or be communicably coupled with an application server, e-mail server, web server, caching server, streaming data server, business intelligence (BI) server, or other server (or a combination of servers). [0099] The computer 902 can receive requests over the network 930 from a client application (for example, executing on another computer 902) and responding to the received requests by processing the said requests in an appropriate software application. In addition, requests may also be sent to the computer 902 from internal users (for example, from a command console or by other appropriate access method), external or third-parties, or other automated applications, as well as any other appropriate entities, individuals, systems, or computers. [00100] Each of the components of the computer 902 can communicate using a system bus 903. In some implementations, any or all of the components of the computer 902, whether hardware or software (or a combination of hardware and software), may interface with each other or the interface 904 (or a combination of both) over the system bus 903 using an application programming interface (API) 912 or a service layer 913 (or a combination of the API 912 and service layer 913). The API 912 may include specifications for routines, data structures, and object classes. The API 912 may be either computer- language independent or dependent and refer to a complete interface, a single function, or even a set of APIs. The service layer 913 provides software services to the computer 902 or other components (whether or not illustrated) that are communicably coupled to the computer 902. The functionality of the computer 902 may be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer 913, provide reusable, defined business functionalities through a defined interface. For example, the interface may be software written in JAVA, C++, or other suitable language providing data in extensible markup language (XML) format or other suitable format. While illustrated as an integrated component of the computer 902, alternative implementations may illustrate the API 912 or the service layer 913 as stand- alone components in relation to other components of the computer 902 or other components (whether or not illustrated) that are communicably coupled to the computer 902. Moreover, any or all parts of the API 912 or the service layer 913 may be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of this disclosure. [00101] The computer 902 includes an interface 904. Although illustrated as a single interface 904 in FIG.7, two or more interfaces 904 may be used according to particular needs, desires, or particular implementations of the computer 902. The interface 904 is used by the computer 902 for communicating with other systems in a distributed environment that are connected to the network 930. Generally, the interface (904 includes logic encoded in software or hardware (or a combination of software and hardware) and operable to communicate with the network 930. More specifically, the interface (604) may include software supporting one or more communication protocols associated with communications such that the network 930 or interface's hardware is operable to communicate physical signals within and outside of the illustrated computer 902. [00102] The computer 902 includes at least one computer processor 905. Although illustrated as a single computer processor 905 in FIG.7, two or more processors may be used according to particular needs, desires, or particular implementations of the computer 902. Generally, the computer processor 905 executes instructions and manipulates data to perform the operations of the computer 902 and any algorithms, methods, functions, processes, flows, and procedures as described in the instant disclosure. [00103] The computer 902 also includes a memory 906 that holds data for the computer 902 or other components (or a combination of both) that can be connected to the network 930. For example, memory 906 can be a database storing data consistent with this disclosure. Although illustrated as a single memory 906 in FIG.7, two or more memories may be used according to particular needs, desires, or particular implementations of the computer 902 and the described functionality. While memory 906 is illustrated as an integral component of the computer 902, in alternative implementations, memory 906 can be external to the computer 902. [00104] The application 907 is an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer 902, particularly with respect to functionality described in this disclosure. For example, application 907 can serve as one or more components, modules, applications, etc. Further, although illustrated as a single application 907, the application 907 may be implemented as multiple applications 907 on the computer 902. In addition, although illustrated as integral to the computer 902, in alternative implementations, the application 907 can be external to the computer 902. [00105] There may be any number of computers 902 associated with, or external to, a computer system containing computer 902, each computer 902 communicating over network 930. Further, the term “client,” “user,” and other appropriate terminology may be used interchangeably as appropriate without departing from the scope of this disclosure. Moreover, this disclosure contemplates that many users may use one computer 902, or that one user may use multiple computers 902. [00106] In some embodiments, the computer 902 is implemented as part of a cloud computing system. For example, a cloud computing system may include one or more remote servers along with various other cloud components, such as cloud storage units and edge servers. In particular, a cloud computing system may perform one or more computing operations without direct active management by a user device or local computer system. As such, a cloud computing system may have different functions distributed over multiple locations from a central server, which may be performed using one or more Internet connections. More specifically, cloud computing system may operate according to one or more service models, such as infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), mobile "backend" as a service (MBaaS), serverless computing, artificial intelligence (AI) as a service (AIaaS), and/or function as a service (FaaS). [00107] Embodiments of the present disclosure may provide at least one of the following advantages. [00108] The present data-based methods and systems for chemical process monitoring do not require knowledge of the fundamental mathematical model of the process. Instead, these methods depend on the historical process data to extract important features of a process operation. These methods are useful for abnormality detection and diagnosis and have been applied to various chemical engineering applications including distillation columns, reactors, valves, and sensors, among others. EXAMPLE [00109] Polymerization of latex was carried out using a batch process in a reactor having three jackets such as shown in FIG. 3. The reactor was an emulsion polymerization reactor. Data was obtained at increments of 0.3 hr from 0 hr to 39.51 hr for 10 different sensor parameters to total an array of size 11x1320 = 13,120, including time. Data was obtained at irregular increments of time for charging parameters, where the data was contained in an array 72x6 = 432, including time. There were 7 fixed known parameters. Each parameter was a monitored and/or diagnostic condition. The different parameters included sensor parameters, charging parameters, and fixed known parameters. The sensor parameters were reactor temperature (°C), reactor pressure (psia), first jacket inlet temperature (°C), first jacket outlet temperature (°C), first jacket flowrate (GPM), second/third jacket outlet temperature (°C), second/third jacket flowrate (GPM), RW temperature (second/third jacket inlet temperature) (°C), inhibitor pot level (%), and monomer 1 separator tank (%). The charging parameters were amount by weight (lbs), temperature of reactor during charging (°C), concentration of sub raw material by weight % (%), and temperature of charged raw material (°C). Various different materials suitable for latex polymerization were charged to the reactor at appropriate points in time. The fixed known parameters were heat of reaction for monomer 1 (BTU/lb), the heat of reaction for monomer 2 (BTU/lb), the heat of reaction for monomer 3 (BTU/lb), the heat of reaction for monomer 4 (BTU/lb), the specific heat of the reactor mixture (Cal/lb·°C), the specific heat of the jacket fluid (Cal/lb·°C), and the total charge in the polymer reactor (lb). [00110] The soft sensor variables estimated were conversion (%), solid content in weight % (%), accumulated heat (Mcal), jacket 1 heat removal (Mcal), jacket 2/3 heat removal (Mcal)., and total heat removed (jacket and raw material addition transfer) (Mcal). [00111] For abnormality detection, the first jacket’s heat removal threshold was 10%, while the second jacket’s heat removal threshold was 7%. The threshold for the soft sensor estimated conversion was 4% and the threshold for the soft sensor estimated solid content was 2.5%. [00112] For testing, the solid content was separately measured for each batch at two points in time in the laboratory as a check on the soft sensor estimation. [00113] The soft sensor results of estimated conversion and solid content are shown in FIGS.8A and 8B for respective batches. The red line in each of the right hand panels in FIGS.8A and 8B represents the estimated solid content by the soft sensor, and the two black dots are the laboratory measurements. The results demonstrate accurate estimation of the solid content with very small estimation error compared to the lab measurement. [00114] An example of abnormality diagnosis result is shown in FIGS. 9A and 9B. An inhibitor carryover from the inhibitor separator tank to reactor caused slow reaction and, as a result, a dead batch in the test/abnormal case. The abnormality diagnosis technique examined the sensor and charging data, and the diagnosis algorithm detected the abnormality at an early stage, as shown in FIG.9A. The heat removal plots for the first jacket clearly show the abnormal deviation of 14%, which is higher than the threshold of 10%. Once the abnormality has been detected, the diagnosis procedure will check the diagnostic conditions to determine the source of the abnormality. The level of the inhibitor separator tank is monitored to diagnose an inhibitor carryover to the reactor abnormality. If there is a considerable variation in the inhibitor separator tank level before and after the monomer 1 is charged, then there is an abnormality. The difference in level between before and after monomer charging is 0.601 percent in the test case, which is higher than the threshold of 0.1 %, as shown in FIG. 9B. As a result, the abnormality diagnosis technique was successful in detecting an abnormality and determining its source. [00115] The term “substantially” can mean an amount of about 80%, such as about 90%, or about 95%, or about 99%, by mole, of a compound or class of compounds in a stream. [00116] As used here and in the appended claims, the words “comprise,” “has,” and “include” and grammatical variations thereof are each intended to have an open, non- limiting meaning that does not exclude additional elements or steps. [00117] “Optionally” means that the subsequently described event or circumstances may or may not occur. The description includes instances where the event or circumstance occurs and instances where it does not occur. [00118] When the words “approximately” and/or “about” are used, this term may mean that there can be a variance in value of up to ±10%, of up to 5%, of up to 2%, of up to 1%, of up to 0.5%, of up to 0.1%, or up to 0.01%. [00119] Ranges may be expressed as from about one particular value to about another particular value, inclusive. When such a range is expressed, it should be understood that another one or more embodiments is from the one particular value to the other particular value, along with all particular values and combinations thereof within the range. [00120] Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from this invention. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of intention of the applicant not to invoke 35 U.S.C. § 112(f) for any limitations of any of the claims herein, except for those in which the claim expressly uses the words ‘means for’ together with an associated function. [00121] It is noted that one or more of the following claims utilize the term “where” or “in which” as a transitional phrase. For the purposes of defining the present technology, it is noted that this term is introduced in the claims as an open-ended transitional phrase that is used to introduce a recitation of a series of characteristics of the structure and should be interpreted in like manner as the more commonly used open-ended preamble term “comprising.” For the purposes of defining the present technology, the transitional phrase “consisting of” may be introduced in the claims as a closed preamble term limiting the scope of the claims to the recited components or steps and any naturally occurring impurities. For the purposes of defining the present technology, the transitional phrase “consisting essentially of” may be introduced in the claims to limit the scope of one or more claims to the recited elements, components, materials, or method steps as well as any non-recited elements, components, materials, or method steps that do not materially affect the novel characteristics of the claimed subject matter. The transitional phrases “consisting of” and “consisting essentially of” may be interpreted to be subsets of the open-ended transitional phrases, such as “comprising” and “including,” such that any use of an open-ended phrase to introduce a recitation of a series of elements, components, materials, or steps should be interpreted to also disclose recitation of the series of elements, components, materials, or steps using the closed terms “consisting of” and “consisting essentially of.” For example, the recitation of a composition “comprising” components A, B, and C should be interpreted as also disclosing a composition “consisting of” components A, B, and C as well as a composition “consisting essentially of” components A, B, and C. Any quantitative value expressed in the present application may be considered to include open-ended embodiments consistent with the transitional phrases “comprising” or “including” as well as closed or partially closed embodiments consistent with the transitional phrases “consisting of” and “consisting essentially of.” The words “comprise,” “has,” and “include” and grammatical variations thereof are each intended to have an open, non-limiting meaning that does not exclude additional elements or steps. [00122] While one or more embodiments of the present disclosure have been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this disclosure, will appreciate that other embodiments can be devised, which do not depart from the scope of the disclosure. Accordingly, the scope of the disclosure should be limited only by the attached claims.