Inter-Communicative Decentralized Multi-Scale Control (ICD-MSC) Scheme: A new approach to overcome MIMO process interactions

Decentralized PID control has been extensively used in process industry due to its functional simplicity. But designing an effective decentralized PID control system is very challenging because of process interactions and dead times, which often impose limitations on control performance. In practice, to alleviate the detrimental effect of process interactions on control performance, decoupling controllers are often incorporated into a decentralized control scheme. In many cases, these conventional decoupling controllers are not physically realizable or too complex for practical implementation. In this paper, we propose an alternative scheme to overcome the performance limitation imposed by process interactions. This new control scheme is extended from the SISO multi-scale control scheme previously developed for nonminimum-phase processes. The salient feature of the new control scheme lies in its communicative structure enabling collaborative communication among all the sub-controllers in the system. This communicative structure serves the purpose of reducing the detrimental effect of process interactions leading to improved control performance and performance robustness. Extensive numerical study shows that the new control scheme is able to outperform some existing decentralized control schemes augmented with traditional decoupling controllers

Multi-objective optimization and model-based predictive control using state feedback linearization for crystallization

The ongoing Quality-by-Design paradigm shift in the pharmaceutical industry has sparked a new interest in exploring advanced process control techniques to aid the efficient manufacture of high value products. One important process in the manufacturing is crystallization, a key process in purification of active pharmaceutical ingredients (APIs). There has been little crystallization control research in the area of global input/output linearization, otherwise referred to as state-feedback linearization (SFL). The global linearization allows a nonlinear model to be linearized over the whole domain for which the model is valid and can be embedded into a model predictive controller (MPC). MPC allows the control of a process based on a model which captures the physical understanding and constraints, but a widely reported challenge with the SFL technique is the poor ability of explicitly handling the plant constraints, which is not ideal for a highly regulated production environment such as pharmaceutical manufacturing. Therefore, the first purpose of this research is to explore the use of SFL and how it can be applied to controlling batch and continuous MSMPR crystallization processes with the incorporation of plant constraints in the MPC (named SFL-Plant constraints). The contribution made from this research is the exploration of the SFL MPC technique with successful implementation of SFL-Plant constraints. The novelty in this method is that the technique builds on existing SFL-MPC frameworks to incorporate a nonlinear constraints routine which handles plant constraints. The technique is applied on numerous scenarios of batch and continuous mixed suspension mixed product removal (MSMPR) supersaturation control of paracetamol in water, both seeded and unseeded, which all show that the SFL-Plant constraints technique indeed produces feasible control over crystallization subject to constraints imposed by limitations such as heat transfer. The SFL-MPC with SFL-Plant constraints was applied to single-input single-output (SISO) and multiple-input multipleoutput (MIMO) systems, demonstrating consistent success across both schemes of control. It was also determined that the SFL-Plant constraints do increase the computational demand by 2 to 5 times that of the SFL when unconstrained. However, the difference in absolute time is not so significant, typically an MPC which acted on a system each minute required less than 5 seconds of computation time with inclusion of SFL-Plant constraints. This technique 5 presents the opportunity to use the SFL-MPC with real system constraints with little additional computation effort, where otherwise this may have not been possible. A further advancement in this research is the comparison between the SFL-MPC technique to an MPC with a data-driven model - AutoRegression model with eXogenous input (ARX) – which is widely used in industry. An ARX model was identified for batch supersaturation control using a batch crystallization model of paracetamol in isopropyl alcohol (IPA) in gPROMS Formulated Products as the plant, and an ARX model developed in an industrial software for advanced process control – PharmaMV. The ARX-MPC performance was compared with SFL-MPC performance and it was found that although the ARX-MPC performed well when controlling a process which operated around the point the ARX-MPC was initially identified, the capability of tracking the supersaturation profile deteriorated when larger setpoints were targeted. SFL-MPC, on the other hand, saw some deterioration in performance quantified through an increase in output tracking error, but remained robust at tracking a wide range of supersaturation targets, thus outperforming the ARX-MPC for trajectory tracking control. Finally, single-objective and multi-objective optimization of a batch crystallization process is investigated to build on the existing techniques. Two opportunities arose from the literature review. The first was the use of variable-time decision variables in optimization, as it appears all pre-existing crystallization optimization problems to determine the ideal crystallization temperature trajectory for maximising mean-size are constructed of piecewise-constant or piecewise-continuous temperature profiles with a fixed time step. In this research the timestep was added as a decision variable to the optimization problem for each piecewise continuous ramp in the crystallization temperature profile and the results showed that for the maximisation of mean crystal length in a 300-minute batch simulation, when using 10 temperature ramps each of variable length resulted in a 20% larger mean size than that of 10 temperature ramps, each over a fixed time length. The second opportunity was to compare the performance of global evolution based Nondominated Sorting Genetic Algorithm – II (NSGA-II) with a deterministic SQP optimization method and a further hybrid approach utilising first the NSGA-II and then the SQP algorithm. It was found that for batch crystallization optimization, it is possible for the SQP to converge a global solution, and the convergence can be guaranteed in the shortest time with little compromise using the hybrid 6 method if no information is known about the process. The NSGA-II alone required excessive time to reach a solution which is less refined. Finally, a multi-objective optimization problem is formed to assess the ability to gain insight into crystallization operation when there are multiple competing objectives such as maximising the number weighted mean size and minimizing the number weighted coefficient of variation in size. The insight gained from this is that it is more time efficient to perform single-objective optimization on each objective first and then initialize the multi-objective NSGA-II algorithm with the single-objective optimal profiles, because this results in a greatly refined solution in significantly less time than if the NSGA-II algorithm was to run without initialization, the results were approximately 20% better for both mean size and coefficient of variation in 10% of the time with initialization

Learning-Based Controller Design with Application to a Chiller Process

In this thesis, we present and study a few approaches for constructing controllers for uncertain systems, using a combination of classical control theory and modern machine learning methods. The thesis can be divided into two subtopics. The first, which is the focus of the first two papers, is dual control. The second, which is the focus of the third and last paper, is multiple-input multiple-output (MIMO) control of a chiller process. In dual control, the goal is to construct controllers for uncertain systems that in expectation minimize some cost over a certain time horizon. To achieve this, the controller must take into account the dual goals of accumulating more information about the process, by applying some probing input, and using the available information for controlling the system. This is referred to as the exploration-exploitation trade-off. Although optimal dual controllers in theory can be computed by solving a functional equation, this is usually intractable in practice, with only some simple special cases as exceptions. Therefore, it is interesting to examine methods for approximating optimal dual control. In the first paper, we take the approach of approximating the value function, which is the solution of the functional equation that can be used to deduce the optimal control, by using artificial neural networks. In the second paper, neural networks are used to represent and estimate hyperstates, which contain information about the conditional probability distributions of the system uncertainties. The optimal dual controller is a function of the hyperstate, and hence it should be useful to have a representation of this quantity when constructing an approximately optimal dual controller. The hyperstate transition model is used in combination with a reinforcement learning algorithm for constructing a dual controller from stochastic simulations of a system model that includes models of the system uncertainties. In the third paper, we suggest a simple reinforcement learning method that can be used to construct a decoupling matrix that allows MIMO control of a chiller process. Compared to the commonly used single-input single-output (SISO) structures, these controllers can decrease the variations in some system signals. This makes it possible to run the system at operating points closer to some constraints, which in turn can enable more energy-efficient operation

Decoupling PID Control of a Reactive Packed Distillation Column

This work has been carried out to demonstrate the application ofdecouplers in the temperature control of a reactive packed distillation column using PID controllers and the production of ethyl acetate as the case study. Ziegler-Nichols and Cohen-Coon techniques were used to calculate the tuning parameters of the controllers for simulation with the aid of MATLAB/Simulink. In the set-point tracking study, top segment, reaction segment and bottom segment temperatureswere the controlled variables while reflux ratio, feed ratio and reboiler duty were chosen as the manipulated variables. For the disturbance rejection study, the reboiler duty was chosen as the disturbance variable. After the simulation, the results obtained from the simulations showed that decoupling PID control has been successfully applied to the reactive packed distillation column. In addition, the lower values of IAEs and ISEs of Cohen-Coon tuning technique revealed that it was better than Ziegler-Nichols tuning technique for this proces

Multivariable PID control of an Activated Sludge Wastewater Treatment Process

In general, wastewater treatment plant (WWTP) consists of several stages before it is released to a receiving water body. There are, preliminary and primary treatment (mechanical treatment), a secondary treatment (biological treatment) and a tertiary treatment (chemical treatment). In this chapter, since the work involve of identification and control design of activated sludge process to improve the performance of the system, and most of the control priorities are centred on the biological treatment process, only the secondary treatment will be highlighted

Nonlinear predictive control applied to steam/water loop in large scale ships

In steam/water loop for large scale ships, there are mainly five sub-loops posing different dynamics in the complete process. When optimization is involved, it is necessary to select different prediction horizons for each loop. In this work, the effect of prediction horizon for Multiple-Input Multiple-Output (MIMO) system is studied. Firstly, Nonlinear Extended Prediction Self-Adaptive Controller (NEPSAC) is designed for the steam/water loop system. Secondly, different prediction horizons are simulated within the NEPSAC algorithm. Based on simulation results, we conclude that specific tuning of prediction horizons based on loop’s dynamic outperforms the case when a trade-off is made and a single valued prediction horizon is used for all the loops

Decoupled Reference Governors for Multi-Input Multi-Output Systems

In this work, a computationally efficient solution for constraint management of square multi-input multi-output (MIMO) systems is presented. The solution, referred to as the Decoupled Reference Governor (DRG), maintains the highly-attractive computational features of scalar reference governors (SRG) compared to Vector Reference Governor (VRG) and Command Governor (CG). This work focuses on square MIMO systems that already achieve the desired tracking performance. The goal of DRG is to enforce output constraints and simultaneously ensure that the degradation to tracking performance is minimal. DRG is based on decoupling the input-output dynamics of the system so that every channel of the system can be viewed as an independent input-output relationship, followed by the deployment of a bank of scalar reference governors for each decoupled channel. We present a detailed set-theoretic analysis of DRG, which highlights its main characteristics. A quantitative comparison between DRG, SRG, and the VRG is also presented in order to illustrate the computational advantages of DRG. Finally, a distillation process is introduced as an example to illustrate the applicability of DRG