Iterative Nonlinear Control of a Semibatch Reactor. Stability Analysis

This paper presents the application of Iterative Nonlinear Model Predictive Control, INMPC, to a semibatch chemical reactor. The proposed control approach is derived from a model-based predictive control formulation which takes advantage of the repetitive nature of batch processes. The proposed controller combines the good qualities of Model Predictive Control (MPC) with the possibility of learning from past batches, that is the base of Iterative Control. It uses a nonlinear model and a quadratic objective function that is optimized in order to obtain the control law. A stability proof with unitary control horizon is given for nonlinear plants that are affine in control and have linear output map. The controller shows capabilities to learn the optimal trajectory after a few iterations, giving a better fit than a linear non-iterative MPC controller. The controller has applications in repetitive disturbance rejection, because they do not modify the model for control purposes. In this application, some experiments with a disturbance in inlet water temperature has been performed, getting good results.Ministerio de Ciencia y Tecnología DPI2004-07444-C04-0

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

Model‐based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low‐quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics‐based and data‐driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high‐quality data by correcting raw process measurements via a physics‐based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data‐driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re‐fitting the simple kinetic model (soft sensor) using the data‐driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed‐batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open‐loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application

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

Stability criterion for the intensification of batch processes with model predictive control

Thermal runaways in batch processes can lead to significant issues for safety and performance during normal operation in industry. This is usually circumvented by running such processes at lower temperatures than necessary, hence losing the opportunity to intensify production and therefore reduce reaction time. The detection of the thermal stability of batch systems can potentially be embedded in an advanced control scheme, therefore improving the performance by being able to intensify the process, achieving higher yields while keeping a stable operation. The derivation of stability criterion K for high-order reactions is presented in this work, resulting in better control when embedded in Model Predictive Control (MPC) schemes than standard nonlinear MPC schemes, based on the work in Kahm and Vassiliadis (2018). The non-trivial extension of stability criterion K for multi-component reactions with application to MPC systems is discussed in detail. The logic and verification of the form of the resultant Damkohler number in particular is discussed and demonstrated with case studies. A comparison of various MPC schemes is presented, showcasing that the implementation using criterion K results in intensified processes kept stable at all times, whilst reducing computational cost with regards to standard nonlinear MPC schemes. Furthermore, reaction times are reduced by at least two-fold with respect to processes run at constant temperatures

Process automation and control strategy by Quality-by-Design in total continuous mRNA manufacturing platforms

Vaccine supply has a bottleneck in manufacturing capacity due to operation personnel and chemicals needed. Assessment of existing mRNA (messenger ribonucleic acid) vaccine processing show needs for continuous manufacturing processes. This is enabled by strict application of the regulatory demanded quality by design process based on digital twins, process analytical technology, and control automation strategies in order to improve process transfer for manufacturing capacity, reduction out-of-specification batch failures, qualified personnel training and number, optimal utilization of buffers and chemicals as well as speed-up of product release. In this work, process control concepts, which are necessary for achieving autonomous, continuous manufacturing, for mRNA manufacturing are explained and proven to be ready for industrialization. The application of the process control strategies developed in this work enable the previously pointed out benefits. By switching from batch-wise to continuous mRNA production as was shown in previous work, which was the base for this study, a potential cost reduction by a factor 5 (i.e., from EUR 0.380 per dose to EUR 0.085 per dose) is achievable. Mainly, based on reduction of personnel (factor 30) and consumable (factor 7.5) per campaign due to the significant share of raw materials in the manufacturing costs (74–97). Future research focus following this work may be on model-based predictive control to gain further optimization potential of potential batch failure and out of specification (OOS) number reduction

Thermal stability criteria embedded in avanced control systems for batch process intensification

Thermal stability of batch processes is a major factor for the safe and efficient production of polymers and pharmaceutical chemicals. The prediction of thermal stability for such nonlinear, non steady-state processes is unreliable when using most stability criteria found in literature. A new stability criterion $\mathcal{K}$ is proposed. This is derived for complex reaction networks based on the divergence criterion. Lyapunov exponents are an alternative method to predict thermal runaway behaviour. Embedding thermal stability criteria within Model Predictive Control (MPC) frameworks results in advanced control systems capable of intensifying batch processes safely, hence resulting in shorter processing times. It is shown that embedding criterion $\mathcal{K}$ within MPC results in more efficient computational times than embedding Lyapunov exponents. Lyapunov exponents potentially can be applied to systems different from chemical exothermic batch reactors due to the general mathematical form. The effect of parametric uncertainty for process control is of utmost importance for industrial application. It is shown that the use of scenario-based MPC and worst case MPC, together with criterion $\mathcal{K}$ and Lyapunov exponents, results in a robust control scheme capable of intensifying batch processes whilst keeping them under control subject to parametric uncertainty. Both, scenario-based and worst case MPC with these two criteria resulted in safe control capable of intensifying batch processes. It is found that worst case MPC embedded with criterion $\mathcal{K}$ results in the most computationally efficient robust control scheme for the intensification of processes considered in this work.Engineering and Physical Sciences Research Council (EPSRC) (DTP – University of Cambridge, Funder reference EP/M508007/1)

Application of iterative nonlinear model predictive control to a batch pilot reactor

IFAC WORLD CONGRESS (16) (16.2005.PRAGA, REPÚBLICA CHECA)The aim of this article is to present the Iterative Model Predictive Controller, inmpc, as a good candidate to control chemical batch reactors. The proposed control approach is derived from a model-based predictive control formulation which takes advantage of the repetitive nature of batch processes. The proposed controller combines the good qualities of Model Predictive Control (mpc) with the possibility of learning from past batches, that is the base of Iterative Control. It uses a nonlinear model and a quadratic objective function that is optimized in order to obtain the control law. The controller is tested on a batch pilot reactor, and a comparison with an Iterative Learning Controller (ilc) is made. Under input constraints and for this nonlinear plant, a fast convergence rate is obtained with the proposed controller, showing good operational results. Although the controller is designed for discrete-time systems, it is a necessary condition that the continuous-time model does not present blow-up characteristics. The batch pilot reactor emulates an exothermal chemical reaction by means of electrical heating

Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization

In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference

The Enhanced Definition and Control of Downstream Processing Operations

Monitoring product and contaminants is critically important at all stages of bioprocess operation, development and control. The availability of rapid measurements on product and key contaminants will yield a higher resolution of data points and will allow for more intelligent operation of a process and thereby enhance the definition and characterisation of a bioprocess. The need to control a bioseparation process is due to the variable nature of upstream conditions, process additives and sub-optimal performance of processing equipment which may lead to different requirements for the operating conditions either within batches or on batch to batch basis. Potential operations for downstream processing of intracellular proteins are the selective flocculation, packed bed and expanded bed chromatographic operations. These processes involve the removal of a large number of contaminants in a single dynamic step and hence are difficult unit operations to characterise and operate in an efficient and reproducible manner. In order to achieve rapid charactensation and control of these processes some form of rapid monitoring was required. A sampling and monitoring system for analysis of an enzyme produced intracellularly in S.cerevisiae, alcohol dehydrogenase (ADH), cell debris, protein and RNA contaminants has been constructed, with a measurement cycle time of 135 s. Both an extended Kalman filter and the Levenberg-Marquardt nonlinear least squares model parameter identification technique have been implemented for rapid process characterisation. Estimation of model parameters from at-line data enabled process performance predictions to be represented in an optimum graphical manner and the subsequent determination of ideal operating conditions in a feedback model based control configuration. The application of such a control strategy for the batch flocculation process yielded on average 92% accuracy in achieving optimum operating conditions. A structured and intelligent use of the at-line data would improve process characterisation in terms of speed and stability. It was demonstrated that rapid monitoring of the packed and expanded bed chromatographic operations yielded improved characterisation in terms of higher resolution data points, enabled real time process analysis and control of the load cycle. For the control of the expanded bed operation a predictive technique was applied to compensate for the large dead volume associated with this unit operation. The feedback control resulted in approximately 80% accurate breakthrough setpoint regulation

Iterative nonlinear model predictive control of a PH reactor. A comparative analysis

IFAC WORLD CONGRESS (16) (16.2005.PRAGA, REPÚBLICA CHECA)This paper describes the control of a batch pH reactor by a nonlinear predictive controller that improves performance by using data of past batches. The control strategy combines the feedback features of a nonlinear predictive controller with the learning capabilities of run-to-run control. The inclusion of real-time data collected during the on-going batch run in addition to those from the past runs make the control strategy capable not only of eliminating repeated errors but also of responding to new disturbances that occur during the run. The paper uses these ideas to devise an integrated controller that increases the capabilities of Nonlinear Model Predictive Control (NMPC) with batch-wise learning. This controller tries to improve existing strategies by the use of a nonlinear controller devised along the last-run trajectory as well as by the inclusion of filters. A comparison with a similar controller based upon a linear model is performed. Simulation results are presented in order to illustrate performance improvements that can be achieved by the new method over the conventional iterative controllers. Although the controller is designed for discrete-time systems, it can be applied to stable continuous plants after discretization