GRAPHICAL TECHNIQUE FOR MODELING INTEGRATING (Non-SELF-REGULATING) PROCESSES WITHOUT STEADY-STATE PROCESS DATA

Model-fitting techniques for controller tuning that require the process to be initially at steady state cannot generally be used with integrating (non-self-regulating) processes. To address this issue, a graphical model-fitting technique is detailed and demonstrated for determination of first order plus dead time integrating model parameters from integrating process response plots. The resulting model parameters can be used directly in a range of tuning correlations designed specifically for integrating processes. The advantage of this technique is that it requires only two periods of constant manipulated and disturbance variables sustained just long enough for the process variable to respond and establish a clear slope. This is an important benefit because integrating processes generally cannot be maintained at an initial steady state as required when using techniques published for self-regulating processes. The result is an industry-friendly method. The method is demonstrated for level control in a pumped tank, a classical challenge in industrial practice. Both a simulation and a bench-scale experimental system are used in the demonstration studies.     

MULTILEVEL PCA AND INDUCTIVE LEARNING FOR KNOWLEDGE EXTRACTION FROM OPERATIONAL DATA OF BATCH PROCESSES

A new methodology for monitoring batch processes is presented which is based on analysis of historical operational data using both principal component analysis (PCA) and inductive learning. Historical data of batch operations are analysed according to stages. For each stage, PCA is employed to analyse the trajectories of each variable over all batch runs and groups the trajectories into clusters. The first one or two PCs for all variables at a stage are then used in further PCA analysis to project the operation of the stage onto operational spaces. Production rules are generated to summarise the operational routes to produce product recipes, and to describe variables' contributions to stage-wise state spaces. A method for automatic identification of stages using wavelet multi-scale analysis is also described. The methodology is illustrated by reference to a case study of a semi-batch polymerisation reactor.     

SENSITIVITY ANALYSIS OF OPTIMAL CONTROL POLICIES FOR BATCH PROCESSES

The computation of optimal control policies for batch processes, critically depends on the process model employed. The real process may deviate from the assumed model due to external signal or model parameter variations. In this paper we examine the effects of these variations on

(a) the value of the objective function, when the nominal optimal policy is applied

(b) the optimum value of the objective function.

We present quantitative relations between the perturbed and the nominal problems and demonstrate their use through a number of examples.     

ANALYSIS OF BATCH CRYSTALLIZATION PROCESSES

The analysis of batch crystallization processes normally requires the consideration of the time-dependent, batch conservation equations (e.g., population, mass, and energy balances), together with appropriate nucleation and growth kinetic equations. The solution of these integro-differential equations is relatively difficult, even by numerical techniques. This review outlines the advances that have been made in the experimental techniques and data analyses which can be used to study the crystallization kinetics and the crystal size distribution (CSD) in batch suspension crystallizers. Several simple and useful methods are discussed which include characterization of CSD maximum, cumulative CSD approach, SSBCR crystallizer, thermal response technique, maximum allowable growth rate, and desupersaturation curve technique.     

ANALYSIS OF BATCH CRYSTALLIZATION PROCESSES

The analysis of batch crystallization processes normally requires the consideration of the time-dependent, batch conservation equations (e.g., population, mass, and energy balances), together with appropriate nucleation and growth kinetic equations. The solution of these integro-differential equations is relatively difficult, even by numerical techniques. This review outlines the advances that have been made in the experimental techniques and data analyses which can be used to study the crystallization kinetics and the crystal size distribution (CSD) in batch suspension crystallizers. Several simple and useful methods are discussed which include characterization of CSD maximum, cumulative CSD approach, SSBCR crystallizer, thermal response technique, maximum allowable growth rate, and desupersaturation curve technique.     

DEVELOPMENT OF BATCH EMULSION POLYMERIZATION PROCESSES

Although emulsion polymerization has been used for a long time, relatively little attention has been paid to the technological issues of this polymerization technique. This paper describes the research on chemical engineering aspects of emulsion polymerization in (semi-)batchwise operated stirred tanks. The objective of this work was to improve the operation of current processes and to allow for improvements in the development of novel emulsion polymerization processes. For this purpose, different issues have shown to be important, for which the work described in this paper has been focused on four topics: emulsification, colloidal stability, rheology in high solids polymerization and heat transfer. These topics have been studied using the polymerization of styrene and vinyl acetate as two representative model systems

Our results reveal that sufficient emulsification is essential for proper control of the polymerization process. For the emulsifier used in this study, the colloidal stability of the polymer particles is mainly governed by the physico-chemical properties of the reaction mixture. During high solids emulsion polymerization, the particle size distribution of the polymer particles considerably influences the Theological properties of the reaction mixture and thereby the flow pattern in the reactor. Heat transfer to the reactor wall depends strongly on reactor geometry, impeller type and diameter as well as stirrer speed. Additionally, the physical properties of the reaction mixture, being related to solids content, conversion and monomer type, are important for heat transfer.     

DEVELOPMENT OF BATCH EMULSION POLYMERIZATION PROCESSES

Although emulsion polymerization has been used for a long time, relatively little attention has been paid to the technological issues of this polymerization technique. This paper describes the research on chemical engineering aspects of emulsion polymerization in (semi-)batchwise operated stirred tanks. The objective of this work was to improve the operation of current processes and to allow for improvements in the development of novel emulsion polymerization processes. For this purpose, different issues have shown to be important, for which the work described in this paper has been focused on four topics: emulsification, colloidal stability, rheology in high solids polymerization and heat transfer. These topics have been studied using the polymerization of styrene and vinyl acetate as two representative model systems

Our results reveal that sufficient emulsification is essential for proper control of the polymerization process. For the emulsifier used in this study, the colloidal stability of the polymer particles is mainly governed by the physico-chemical properties of the reaction mixture. During high solids emulsion polymerization, the particle size distribution of the polymer particles considerably influences the Theological properties of the reaction mixture and thereby the flow pattern in the reactor. Heat transfer to the reactor wall depends strongly on reactor geometry, impeller type and diameter as well as stirrer speed. Additionally, the physical properties of the reaction mixture, being related to solids content, conversion and monomer type, are important for heat transfer.     

FEASIBILITY OF ON-LINE REOPTIMIZATION IN BATCH PROCESSES

This work points out some serious drawbacks of the standard procedure for on-line optimization of batch processes. Commonly, at any given batch time, on-line measurement information is used to improve the estimate of the current state and the objective function is optimized again based on the estimated state. This strategy is useful when only the initial state is in error, but is unreliable in the more common case of an inaccurate dynamic model. Reoptimization based on the state estimates may then constitute a poor policy, even if perfect state estimates were available. Simulation examples of a semi-batch reactor are used to illustrate the associated pitfalls and to demonstrate what can be done to possibly detect and circumvent them.     

DYNAMIC OPTIMAL CONTROL OF BATCH CRYSTALLIZATION PROCESSES

This work applies an on-line optimal control strategy developed by Zhang (2001) to two cooling batch crystallization processes. The algorithm initially finds the optimal crystallizer temperature and subsequently uses a feedback control system in order to achieve the desired final product quality of the crystals expressed in terms of the final crystal size distribution. In both batch processes, it is shown that the on-line optimal control approach provides better final product quality as compared with a simplified optimal cooling policy. The improvement is especially noticeable in the presence of plant/model mismatch or errors in the initial conditions.     

DYNAMIC OPTIMAL CONTROL OF BATCH CRYSTALLIZATION PROCESSES

The estimation of the solids circulation rate in a dual fluidized bed gasifier is essential as it influences the gasification performance. However, such estimation requires experimental data input, which is often not available during the design phase of a fluidized bed. In this work, a correlation was developed based on literature and experimental data (from a cold model dual fluidized bed gasifier) to estimate the solids circulation rate at high solids-to-gas density ratios. The correlation was then validated with literature data from a dual fluidized bed steam gasifier under hot conditions, yielding maximum discrepancies of ±25%.     

OPTIMAL DESIGN AND OPERATION OF BATCH PROCESSES

The economical design of continuous chemical processes to produce commodity products has reached an advanced state of development. Modern computer tools are used routinely to simulate and optimize these processes. This is not the case, however, for the manufacture of speciality products which must be made in batch operations. The continuing shift towards the production of higher value-added specialty products by the CPI has stimulated efforts aimed at developing good computer assisted design strategies for batch processes.

This paper discusses the formulation of the problem for the optimal design and operation of batch processes. The batch problem differs from the continuous one in a number of important ways. First, batch plants do not operate at steady state. There are important trade-offs between the processing time and the severity (intensity) of processing in single units. Cycle time and performance trade-offs also exist among the various units in the process. Second, batch plants produce multiple products in many cases. There is often a competition for shared resources (labor, utilities, and equipment) among the various products. This paper presents a hierarchical solution approach for the design and optimization of a batch process. The approach is demonstrated by solving an example problem which illustrates the fundamental economic trade-offs.     

A HYBRID CFD COMPARTMENTALIZATION MODELING FRAMEWORK FOR THE SCALEUP OF BATCH COOLING CRYSTALLIZATION PROCESSES

Crystallizer design is often hindered by the lack of scaleup rules, hydrodynamic information, and predictive crystallization modeling tools. A hybrid CFD compartmentalization batch cooling crystallizer model is proposed to take into account localized mixing, heat transfer, and fluid hydrodynamics, combined with key process engineering information obtained on a laboratory scale. The compartments were identified using CFD simulations based on the crystallizer geometry and operating conditions. The population, mass, concentration, and energy balance of each compartment is modeled separately as a well-mixed MSMPR unit with input and output streams. The software gPROMS (Process Systems Enterprise Limited) is a process-modeling tool that can facilitate compartmental modeling and will be used for the prediction of the crystallization behavior upon scaleup.     

A HYBRID CFD COMPARTMENTALIZATION MODELING FRAMEWORK FOR THE SCALEUP OF BATCH COOLING CRYSTALLIZATION PROCESSES

Crystallizer design is often hindered by the lack of scaleup rules, hydrodynamic information, and predictive crystallization modeling tools. A hybrid CFD compartmentalization batch cooling crystallizer model is proposed to take into account localized mixing, heat transfer, and fluid hydrodynamics, combined with key process engineering information obtained on a laboratory scale. The compartments were identified using CFD simulations based on the crystallizer geometry and operating conditions. The population, mass, concentration, and energy balance of each compartment is modeled separately as a well-mixed MSMPR unit with input and output streams. The software gPROMS (Process Systems Enterprise Limited) is a process-modeling tool that can facilitate compartmental modeling and will be used for the prediction of the crystallization behavior upon scaleup.     

ESTIMATION OF THE MEMORY PARAMETER FOR NONSTATIONARY OR NONINVERTIBLE FRACTIONALLY INTEGRATED PROCESSES

Abstract. We consider the asymptotic characteristics of the periodogram ordinates of a fractionally integrated process having memory parameter d≥ 0.5, for which the process is nonstationary, or d≤ -.5, for which the process is noninvertible. Series having d outside the range (-.5,.5) may arise in practice when a raw series is modeled without preliminary consideration of the stationarity and invertibility of the series or when a wrong decision is made concerning the stationarity and invertibility of the series. We find that the periodogram of a nonstationary or noninvertible fractionally integrated process at the jth Fourier frequency ωj= 2πj/n, where n is the sample size, suffers from an asymptotic relative bias which depends on j. We also examine the impact of periodogram bias on the regression estimator of d proposed by Geweke and Porter-Hudak (1983) in finite samples. The results indicate that the bias in the periodogram ordinates can strongly affect the GPH estimator even when the number of Fourier frequencies used in the regression is allowed to depend on the length of the series. We find that data tapering and elimination of the first periodogram ordinate in the regression can reduce this bias, at the cost of an increase in variance for nonstationary series. Additionally, we find for nonstationary series that the GPH estimator is more nearly invariant to first-differencing when a data taper is used.     

REDUCTION OF TOMOGRAPHIC DATA FOR USE IN THE CONTROL OF MULTIPHASE PROCESSES

Tomograhic sensors are ideally suited to the on-line control of multiphase processes. Little work to date however has been undertaken to determine what type and style of information is required from an image to provide effective process control. In this paper, a possible strategy is presented; namely, a combination of Principal Component Analysis (PCA) and Neural Networks (NN) is used to convert multivariate data from tomographic images into useful information suitable for the control and optimization of chemical processes.     

ANALYSIS OF BATCH DRYING DATA USING SAS

An analysis technique is described which uses the nonlinear curve fitting capabilities of the Statistical Analysis System (SAS) to analyze batch drying data. The technique does not require any apriori assumptions about whether a constant rate period exists. Rather, the only assumptions necessary are the possible forms of the equations which can be used to describe the drying phenomenon. The program will then determine, based upon statistical techniques, where each equation should be used during the drying process in order to best describe the overall drying process. Two examples are given; the first analyzes the drying of. sand, a material having a constant rate period; the second analyzes the data describing the drying of French fried potato pieces.     

ANALYSIS OF BATCH DRYING DATA USING SAS

An analysis technique is described which uses the nonlinear curve fitting capabilities of the Statistical Analysis System (SAS) to analyze batch drying data. The technique does not require any apriori assumptions about whether a constant rate period exists. Rather, the only assumptions necessary are the possible forms of the equations which can be used to describe the drying phenomenon. The program will then determine, based upon statistical techniques, where each equation should be used during the drying process in order to best describe the overall drying process. Two examples are given; the first analyzes the drying of. sand, a material having a constant rate period; the second analyzes the data describing the drying of French fried potato pieces.     

分批精馏模拟技术

综述了当前分批精馏模拟技术的研究进展。介绍了作为目前快捷（ｓｈｏｒｔｃｕｔ） 模型主流的ＦＵＧ快捷模型和ＩＳＣ 快捷模型。与严格模型相比,ＦＵＧ模型和ＩＳＣ 模型具有适应性强、计算量小、计算精度较高的独特优点。     

LAG TIME AND STEADY-STATE PERMEABILITY FOR A DIFFUSION SYSTEM WITH SERIAL AND/OR PARALLEL PATHS

A diffusion system is considered in which transport occurs along two parallel paths which, in turn, are in series with another path. Relations for the steady-state permeability and lag time are obtained in terms of the cross-sectional areas and lengths of diffusion, diffusivities and interfacial partition coefficients. The relations are derived from the slope and intercept (on the time axis) of the linear asymptote for the total amount of diffusant released into the receiver. The asymptote is calculated by repeated integration rather than a complete solution to the coupled diffusion equations. Equivalence relations are given for the transformation of the parallel paths into a single path, thus allowing for a treatment of the whole system in the form of two homogeneous paths in series. These equivalence relations include an effective partition coefficient as an average of its component's partition coefficient weighted by the corresponding fraction of cross-sectional area, and an effective diffusivity as an average of individual diffusivities weighted by the corresponding fraction of the product of cross-sectional area and partition coefficient.     

THE GENERATION OF CONTROL STEPS IN BATCH PROCESSES BY USING AI TECHNIQUES

A new method for the generation of control steps in batch processes is presented in this paper. One of the AI techniques, namely, the key word analysis technique is used to process the master recipe and the corresponding equipment units. The control steps are created according to the process information specified in the master recipe and the properties of the existing equipment unit. The aim is to unambiguously specify the control steps using a set of basic control instructions from which the actual control code could be created.