Superstructure of yOTs-the network-based chemical process operator training system for multiple trainees

OTS (Operator Training System) is becoming popular for the safe and effective operation of chemical processes and control systems. This paper outlines the total hardware system superstructure and software modules of yOTS (Yonsei Operator Training System) which we developed. yOTS is a network based multi-training system composed of a workstation-based server module and PC-based user modules. The user module has a DCS-like user interface and sends data to OM (yOTS Manager) over the network. Reliability and stability are essential for the successful development of distributed OTS.State-of-the-art technologies of efficiency and stability are mainly considered in this paper. yOTS is superior to other OTS in its ease of handling discrete events, managing process models, expanding module functionality and multi-training over the network. The structure of yOTS and core algorithm for a multiple trainer over the network is also presented. A batch process example is used to illustrate the proposed advantages of yOTS.     

Use of the Exponential Model of Enzyme Decay to Plan a Strategy for Enzymatic Batch Reactions and to Assess the Industrial Applicability of Immobilised Enzyme Preparations

This paper is concerned with the development of a model to plan a strategy for an enzymatic batch process whereby the enzyme is subjected to deactivation as described by the exponential (first-order) decay model. The particular system used was the enzymatic hydrolysis of penicillin to 6-aminopenicillanic acid, but the model can be utilised with other batch systems, provided that decay of the immobilised enzyme preparation is described by the exponential decay model. The model developed is eminently practical and simple, and several examples of its application are given. Experimental data obtained in a small pilot plant batch recirculated reactor could, on average, be well fitted by this model.     

Batch-to-batch optimal control of a batch polymerisation process based on stacked neural network models

A neural network based batch-to-batch optimal control strategy is proposed in this paper. In order to overcome the difficulty in developing mechanistic models for batch processes, stacked neural network models are developed from process operational data. Stacked neural networks have enhanced model generalisation capability and can also provide model prediction confidence bounds. However, the optimal control policy calculated based on a neural network model may not be optimal when applied to the true process due to model plant mismatches and the presence of unknown disturbances. Due to the repetitive nature of batch processes, it is possible to improve the operation of the next batch using the information of the current and previous batch runs. A batch-to-batch optimal control strategy based on the linearisation of stacked neural network model is proposed in this paper. Applications to a simulated batch polymerisation reactor demonstrate that the proposed method can improve process performance from batch to batch in the presence of model plant mismatches and unknown disturbances.     

A general simulation model of batch chemical reactors for thermal control investigations

This paper describes the development of a dynamic simulation model for stirred tank batch or semi-batch chemical reactors fitted with an alternative heating-cooling system. Heat and mass balances are established for the reactor and its jacket. Since the general purpose of our research is the thermal control of these reactors, special attention is devoted to the behaviour of the heating-cooling system. In this article, we are particularly concerned with an alternative system, i.e. different fluids at a constant temperature can be alternatively delivered to the jacket. The computer simulation programme is flexible, enabling simulation of a batch or semi-batch reaction vessel, ranging from a laboratory pilot plant to a full-scale production plant. A control algorithm is included which allows reactor operation with open or closed-loop temperature control. To demonstrate the good performance of the simulation model, experimental results are presented for both a pilot plant and an industrial reactor.     

On-line optimal control of a seeded batch cooling crystallizer

In this paper, an on-line optimal control methodology is developed for the optimal quality control of a seeded batch cooling crystallizer process. An extended Kalman filter is successfully implemented to predict seven unmeasured state variables based on three measurements in the batch process. A PI controller is used in a feedback control system to implement the optimal path. It is found that the PI controller can ensure tracking of the optimal path. The simulation results show that on-line optimal control strategy leads to a substantial improvement of the end product quality expressed in terms of the mean size and the width of the distribution. The effects of the plant/model mismatch and disturbances are also tested and discussed.     

A self tuning controller for multicomponent batch distillation with soft sensor inference based on a neural network

Multicomponent batch distillation is an operation difficult to control not only for its nonlinear and transient behaviour, but because the product quality cannot be measured rapidly and with reliability. In the present work, a computational system for direct digital control is developed for a pilot plant batch distillation column. The development of a self tuning regulator and a soft sensor of composition based on a neural network is described. Top and reboiler temperature measurements are the basis for the on-line composition inference. The computational system was experimentally tested in a computer operated pilot column. It could be seen that the neural network soft sensor is a feasible and a reliable tool to solve on-line operational problems of the control engineering systems. The developed control system permits to operate the batch distillation column efficiently and is easy to be implemented and operated.     

Operator training simulators for biorefineries: current position and future directions

Recent technological advances in the development of alternative energy sources, including biofuels, for transportation and energy requirements have demonstrated the need for highly skilled engineers and operators in the biotechnological industries. Although operator training simulators (OTS) used in the traditional chemical process industries may be used to train biorefinery operators and engineers, several distinct aspects of bioprocess operations make their direct application limited. The development and deployment of OTSs for use in biotechnological processes is therefore beginning to gain increasing attention. This review paper examines the present status of OTS development and use in biorefineries, including future considerations on how an OTS may be used to improve operator competence, maximise biorefinery operational efficiencies and protect people and the environment. The general premise of an OTS is that model‐based operator training simulators can be used to verifiably enhance the training of industrial operators to run complex biorefineries. Only a few examples of the design and application of OTSs in large‐scale biorefineries have so far been reported. A discussion of the mathematical models used for OTS development is briefly presented, as well as available OTS design frameworks and vendors, including their benefits and drawbacks. The review concludes by looking at possible future directions of OTS development and use in biorefineries and their contribution in facilitating the transition to a bio‐based economy. © 2018 Society of Chemical Industry     

Profitability as a criterion of batch process control design

Batch processes have in some cases many advantages in comparison with continuous processes even though continuous processes are becoming common.

The main disadvantages of batch processes are the discontinuous usage of raw materials and energy as well as the discontinuous production thus causing difficulties in power plant and other continuous processes connected with the batch process in question. If there are several parallel process units, difficulties can arise with parallel process unit sequencing and product quality equalization. However, with the aid of computer control these and other disadvantages are eliminated or minimized so that total automation of batch processes is possible.

In this paper the basic principles of batch process control design are considered, with particular emphasis on the economic justification criterion. As an example, a computer control design of sulphate batch digesters is considered. This approach is based on more than 20 implementations of batch process automation.     

Industrial-scale operation of microbial processes

Interactions between fermentation process development and plant design are explored. An attempt is made to identify the various aspects of microbial reaction and material-transport control of process rates. In scale-up some factors remain constant and some can change and the importance of each is discussed. The case for the use of a stirred fermenter in batch processing is developed and the various non-technical constraints that can determine the scale of operation are described. The production of desirable fluid characteristics at different scales of operation are discussed and it is shown that a deviation from geometrical similarity may be necessary for fermentation process scale-up. A design procedure for aeration-limited processes reveals the complex multitude of factors that must be considered in fermentation-equipment design.     

BATCH-TO-BATCH OPTIMAL CONTROL OF BATCH PROCESSES BASED ON RECURSIVELY UPDATED NONLINEAR PARTIAL LEAST SQUARES MODELS

A batch-to-batch optimal control approach for batch processes based on batch-wise updated nonlinear partial least squares (NLPLS) models is presented in this article. To overcome the difficulty in developing mechanistic models for batch/semi-batch processes, a NLPLS model is developed to predict the final product quality from the batch control profile. Mismatch between the NLPLS model and the actual plant often exists due to low-quality training data or variations in process operating conditions. Thus, the optimal control profile calculated from a fixed NLPLS model may not be optimal when applied to the actual plant. To address this problem, a recursive nonlinear PLS (RNPLS) algorithm is proposed to update the NLPLS model using the information newly obtained after each batch run. The proposed algorithm is computationally efficient in that it updates the model using the current model parameters and data from the current batch. Then the new optimal control profile is recalculated from the updated model and implemented on the next batch. The procedure is repeated from batch to batch and, usually after several batches, the control profile will converge to the optimal one. The effectiveness of this method is demonstrated on a simulated batch polymerization process. Simulation results show that the proposed method achieves good performance, and the optimization with the proposed NLPLS model is more effective and stable than that with a batch-wise updated linear PLS model.     

BATCH-TO-BATCH OPTIMAL CONTROL OF BATCH PROCESSES BASED ON RECURSIVELY UPDATED NONLINEAR PARTIAL LEAST SQUARES MODELS

A batch-to-batch optimal control approach for batch processes based on batch-wise updated nonlinear partial least squares (NLPLS) models is presented in this article. To overcome the difficulty in developing mechanistic models for batch/semi-batch processes, a NLPLS model is developed to predict the final product quality from the batch control profile. Mismatch between the NLPLS model and the actual plant often exists due to low-quality training data or variations in process operating conditions. Thus, the optimal control profile calculated from a fixed NLPLS model may not be optimal when applied to the actual plant. To address this problem, a recursive nonlinear PLS (RNPLS) algorithm is proposed to update the NLPLS model using the information newly obtained after each batch run. The proposed algorithm is computationally efficient in that it updates the model using the current model parameters and data from the current batch. Then the new optimal control profile is recalculated from the updated model and implemented on the next batch. The procedure is repeated from batch to batch and, usually after several batches, the control profile will converge to the optimal one. The effectiveness of this method is demonstrated on a simulated batch polymerization process. Simulation results show that the proposed method achieves good performance, and the optimization with the proposed NLPLS model is more effective and stable than that with a batch-wise updated linear PLS model.     

Trajectory tracking of a batch polymerization reactor based on input–output-linearization of a neural process model

Input–output-linearization via state feedback offers the potential to serve as a practical and systematic design methodology for nonlinear control systems. Nevertheless, its widespread use is delayed due to the fact that developing an accurate plant model based on physical principles is often too costly and time consuming. Data-based modeling of dynamic systems using neural networks offers a cost-effective alternative. This work describes the methodology of input–output-linearization using neural process models and gives an extended simulative case study of its application to trajectory tracking of a batch polymerization reactor.     

A combined batch process simulation in a production support environment

Productive and safety production of high quality products is required in batch processes, and the importance of generating control recipes, which defines operations of each batch, is increasing. The control recipe is generated through the evolutionary cycle: initiate operations, evaluate performance and modify operations, and combined batch process simulator plays an important role for evaluating the performance. This paper describes the development of production support environment to generate control recipe, and embedding combined discrete/continuous batch process simulator. Based on the activity model of batch production, a framework of the support environment is proposed, and to date development of embedding combined batch process simulator is explained.     

Tracking the predefined optimal policies for multiple-fraction batch distillation by using adaptive control

Predefined optimal policies will be tracked with control systems to realize the optimum of multiple-fraction batch distillation. Adaptive control is proposed to carry out this task. Characteristics of batch distillation control are analyzed and a proper system is designed for controlling such processes. Besides tracking the optimal reflux ratio profile, the maximum vapor load will be maintained during the batch. In addition, a changing temperature profile of the condenser should be followed to reduce the operating energy with a possibly minimum subcooling. Recursive least square estimation (RLSE) with a variable forgetting factor is applied to the on-line identification of the plant to follow the changing dynamics of the process. Generalized predictive control (GPC) is used to track the predefined policies. The effectiveness of the control strategy is verified with a pilot batch column and the tracking performance is compared with that of PID controllers.     

Emulsion polymerisation in continuous reactor systems

Continuous reactor systems find important applications in the production of sythetic polymer latexes and increased production rates are providing additional incentives for the development of new continuous systems. The fundamental factors that should be considered in the development of continuous processes are reviewed in this paper. Particular emphasis is placed on exploring the differences between batch and continuous reactors and why these difference can lead to different latex product, even if the same recipe is employed. These factors make the task of designing continuous reactor systems based on batch research and development very difficult and risky. Thus it is recommended that small-scale continuous reactors be employed early in a product/process development effort if that effort is likely to lead to a continuous commercial process. The published literature on continuous emulsion polymerization reactors has been reasonably active in the past ten years. Our understanding of the chemistry and reactor modelling has been advanced but a number of areas for fruitful research remains. Some of these problems are reviewed and directions for new efforts are suggested.     

Performance comparison of batch and continuous flow surface aeration systems

The oxygen transfer rate and the corresponding power requirement to operate the rotor are vital for design and scale-up of surface aerators. The aeration process can be analyzed in two ways such as batch and continuous systems. The process behaviors of batch and continuous flow systems are different from each other. The experimental and numerical results obtained through the batch systems cannot be relied on and applied for the designing of the continuous aeration tank. Based on the experimentation on batch and continuous type systems, the present work compares the performance of both the batch and continuous surface aeration systems in terms of their oxygen transfer capacity and power consumption. A simulation equation developed through experimentation has shown that continuous flow surface aeration systems are taking more energy than the batch systems. It has been found that batch systems are economical and better for the field application but not feasible where large quantity of wastewater is produced.     

Transfer of process monitoring models between plants: Batch systems

In this paper, the problem of transferring a process monitoring model between different batch plants is addressed. By exploiting the data coming from a source plant similar to the target plant to which a batch production needs to be transferred, it is possible to transfer a process monitoring model to the target plant under the assumption that the source plant and the target plant are driven by the same fundamental laws. Two transfer methodologies are proposed. The first one is based on multiway principal component analysis, and exploits the information of similar (“common”) variables that are measured both in the source plant and in the target plant. The second methodology is based on the use of multiway joint-Y partial least-squares regression and exploits all the measured data, i.e. common variables as well as variables that are measured only in one of the two plants. The effectiveness of the proposed methodologies is demonstrated using a benchmark simulated fed-batch fermentation process for penicillin production.     

合成氨和尿素装置仿真培训系统的开发及应用

论述了合成氨和尿素装置仿真培训系统(OTS)开发过程中具体建模的范围以及建模所用方法,其中包括装置各动、静设备模型中用到的方法和装置各个系统物料计算所用到的热力学方法。对已成功开发的OTS模拟器在操作员工的培训、控制系统的校验、操作员工能力的测评以及操作员工技能竞赛等方面的应用进行了介绍,同时也对新建装置在OTS开发过程中经常遇到的问题进行了简要说明。