Iterative learning control of crystallisation systems

Under the increasing pressure of issues like reducing the time to market, managing lower production costs, and improving the flexibility of operation, batch process industries thrive towards the production of high value added commodity, i.e. specialty chemicals, pharmaceuticals, agricultural, and biotechnology enabled products. For better design, consistent operation and improved control of batch chemical processes one cannot ignore the sensing and computational blessings provided by modern sensors, computers, algorithms, and software. In addition, there is a growing demand for modelling and control tools based on process operating data. This study is focused on developing process operation data-based iterative learning control (ILC) strategies for batch processes, more specifically for batch crystallisation systems. In order to proceed, the research took a step backward to explore the existing control strategies, fundamentals, mechanisms, and various process analytical technology (PAT) tools used in batch crystallisation control. From the basics of the background study, an operating data-driven ILC approach was developed to improve the product quality from batch-to-batch. The concept of ILC is to exploit the repetitive nature of batch processes to automate recipe updating using process knowledge obtained from previous runs. The methodology stated here was based on the linear time varying (LTV) perturbation model in an ILC framework to provide a convergent batch-to-batch improvement of the process performance indicator. In an attempt to create uniqueness in the research, a novel hierarchical ILC (HILC) scheme was proposed for the systematic design of the supersaturation control (SSC) of a seeded batch cooling crystalliser. This model free control approach is implemented in a hierarchical structure by assigning data-driven supersaturation controller on the upper level and a simple temperature controller in the lower level. In order to familiarise with other data based control of crystallisation processes, the study rehearsed the existing direct nucleation control (DNC) approach. However, this part was more committed to perform a detailed strategic investigation of different possible structures of DNC and to compare the results with that of a first principle model based optimisation for the very first time. The DNC results in fact outperformed the model based optimisation approach and established an ultimate guideline to select the preferable DNC structure. Batch chemical processes are distributed as well as nonlinear in nature which need to be operated over a wide range of operating conditions and often near the boundary of the admissible region. As the linear lumped model predictive controllers (MPCs) often subject to severe performance limitations, there is a growing demand of simple data driven nonlinear control strategy to control batch crystallisers that will consider the spatio-temporal aspects. In this study, an operating data-driven polynomial chaos expansion (PCE) based nonlinear surrogate modelling and optimisation strategy was presented for batch crystallisation processes. Model validation and optimisation results confirmed this approach as a promise to nonlinear control. The evaluations of the proposed data based methodologies were carried out by simulation case studies, laboratory experiments and industrial pilot plant experiments. For all the simulation case studies a detailed mathematical models covering reaction kinetics and heat mass balances were developed for a batch cooling crystallisation system of Paracetamol in water. Based on these models, rigorous simulation programs were developed in MATLAB®, which was then treated as the real batch cooling crystallisation system. The laboratory experimental works were carried out using a lab scale system of Paracetamol and iso-Propyl alcohol (IPA). All the experimental works including the qualitative and quantitative monitoring of the crystallisation experiments and products demonstrated an inclusive application of various in situ process analytical technology (PAT) tools, such as focused beam reflectance measurement (FBRM), UV/Vis spectroscopy and particle vision measurement (PVM) as well. The industrial pilot scale study was carried out in GlaxoSmithKline Bangladesh Limited, Bangladesh, and the system of experiments was Paracetamol and other powdered excipients used to make paracetamol tablets. The methodologies presented in this thesis provide a comprehensive framework for data-based dynamic optimisation and control of crystallisation processes. All the simulation and experimental evaluations of the proposed approaches emphasised the potential of the data-driven techniques to provide considerable advances in the current state-of-the-art in crystallisation control

특정 점의 추적을 위한 모델예측제어가 결합된 새로운 반복학습제어 기법

학위논문 (박사)-- 서울대학교 대학원 : 화학생물공학부, 2017. 2. 이종민.본 논문에서는 제약조건이 있는 다변수 회분식 공정의 제어를 위해 반복학습제어(Iterative learning control, ILC)와 모델예측제어(Model predictive control, MPC)를 결합한 반복학습 모델예측제어(Iterative learning model predictive control, ILMPC)를 다룬다. 일반적인 ILC는 모델의 불확실성이 있더라도 이전 회분의 정보를 이용해 학습하기 때문에 출력을 기준궤적에 수렴시킬 수 있다. 하지만 기본적으로 개루프 제어이기 때문에 실시간 외란을 제거할 수 없다. MPC는 이전 회분의 정보를 이용하지 않기 때문에 모든 회분에서 동일한 성능을 보이며 모델의 정확도에 크게 의존한다. 본 논문에서 ILC와 MPC의 모든 장점을 포함하는 ILMPC를 제안한다. 많은 회분식 또는 반복 공정에서 출력은 모든 시간에서의 기준궤적을 추적할 필요가 없다. 따라서 본 논문에서는 원하는 점에만 수렴할 수 있는 새로운 ILMPC 기법을 제안한다. 제안한 기법을 사용할 경우 원하는 점을 지나는 기준궤적을 만드는 과정이 필요 없게 된다. 또한 본 논문은 점대점 추적, 반복 학습, 제약조건, 실시간 외란 제거 등의 성능을 보이기 위한 다양한 예제를 제공한다.In this thesis, we study an iterative learning control (ILC) technique combined with model predictive control (MPC), called the iterative learning model predictive control (ILMPC), for constrained multivariable control of batch processes. Although the general ILC makes the outputs converge to reference trajectories under model uncertainty, it uses open-loop control within a batchthus, it cannot reject real-time disturbances. The MPC algorithm shows identical performance for all batches, and it highly depends on model quality because it does not use previous batch information. We integrate the advantages of the two algorithms. In many batch or repetitive processes, the output does not need to track all points of a reference trajectory. We propose a novel ILMPC method which can only consider the desired reference points, not an entire reference trajectory. It does not require to generate a reference trajectory which passes through the specific desired points. Numerical examples are provided to demonstrate the performances of the suggested approach on point-to-point tracking, iterative learning, constraints handling, and real-time disturbance rejection.1. Introduction 1 1.1 Background and Motivation 1 1.2 Literature Review 4 1.2.1 Iterative Learning Control 4 1.2.2 Iterative Learning Control Combined with Model Predictive Control 15 1.2.3 Iterative Learning Control for Point-to-Point Tracking 17 1.3 Major Contributions of This Thesis 18 1.4 Outline of This Thesis 19 2. Iterative Learning Control Combined with Model Predictive Control 22 2.1 Introduction 22 2.2 Prediction Model for Iterative Learning Model Predictive Control 25 2.2.1 Incremental State-Space Model 25 2.2.2 Prediction Model 30 2.3 Iterative Learning Model Predictive Controller 34 2.3.1 Unconstrained ILMPC 34 2.3.2 Constrained ILMPC 35 2.3.3 Convergence Property 37 2.3.4 Extension for Disturbance Model 42 2.4 Numerical Illustrations 44 2.4.1 (Case 1) Unconstrained and Constrained Linear SISO System 45 2.4.2 (Case 2) Constrained Linear MIMO System 49 2.4.3 (Case 3) Nonlinear Batch Reactor 53 2.5 Conclusion 59 3. Iterative Learning Control Combined with Model Predictive Control for Non-Zero Convergence 60 3.1 Iterative Learning Model Predictive Controller for Nonzero Convergence 60 3.2 Convergence Analysis 63 3.2.1 Convergence Analysis for an Input Trajectory 63 3.2.2 Convergence Analysis for an Output Error 65 3.3 Illustrative Example 71 3.4 Conclusions 75 4. Iterative Learning Control Combined with Model Predictive Control for Tracking Specific Points 77 4.1 Introduction 77 4.2 Point-to-Point Iterative Learning Model Predictive Control 79 4.2.1 Extraction Matrix Formulation 79 4.2.2 Constrained PTP ILMPC 82 4.2.3 Iterative Learning Observer 86 4.3 Convergence Analysis 89 4.3.1 Convergence of Input Trajectory 89 4.3.2 Convergence of Error 95 4.4 Numerical Examples 98 4.4.1 Example 1 (Linear SISO System with Disturbance) 98 4.4.2 Example 2 (Linear SISO System) 104 4.4.3 Example 3 (Comparison between the Proposed PTP ILMPC and PTP ILC) 107 4.4.4 Example 4 (Nonlinear Semi-Batch Reactor) 113 4.5 Conclusion 119 5. Stochastic Iterative Learning Control for Batch-varying Reference Trajectory 120 5.1 Introduction 121 5.2 ILC for Batch-Varying Reference Trajectories 123 5.2.1 Convergence Property for ILC with Batch-Varying Reference Trajectories 123 5.2.2 Iterative Learning Identification 126 5.2.3 Deterministic ILC Controller for Batch-Varying Reference Trajectories 129 5.3 ILC for LTI Stochastic System with Batch-Varying Reference Trajectories 132 5.3.1 Approach1: Batch-Domain Kalman Filter-Based Approach 133 5.3.2 Approach2: Time-Domain Kalman Filter-Based Approach 137 5.4 Numerical Examples 141 5.4.1 Example 1 (Random Reference Trajectories 141 5.4.2 Example 2 (Particular Types of Reference Trajectories 149 5.5 Conclusion 151 6. Conclusions and Future Works 156 6.1 Conclusions 156 6.2 Future work 157 Bibliography 158 초록 170Docto