An investigation of the interactions between system characteristics and controllability for reactive distillation systems

Reactive distillation is an emerging process intensification technology, although its operation and control are complex due to the interactions between reaction and separation within the column. In this work, the impact of reaction and separation, as well as design parameters, on the controllability of reactive distillation processes is investigated, using a systematic methodology developed. Case studies of industrial interest are considered, varying in the key (reaction, separation and design) parameters, in order to investigate the relative impact of the latter on the controllability of the reactive distillation systems. It is shown that the system with slower kinetics demonstrates an increased difficulty in rejecting feed disturbances for both one point (V-only) and two-point (LV) control configurations. Even when linear model predictive control (MPC) is considered based on a state-space representation of the model, the system with slower reaction kinetics is still more difficult to control, for both set point change and load disturbance. It is also shown that revision of the optimal steady state design variables, such as the total number of stages, may be beneficial for the controllability of the process. The importance of maintaining feed ratio in stoichiometric processes is identified and discussed, as failure to do so may result in failure to maintain both product purities when two point control is considered

Methodologies for the optimisation, control and consideration of uncertainty of reactive distillation

The work presented in this thesis is motivated by the current obstacles hindering the implementation of reactive distillation in industry, mainly related to the complexities of its design and control, as well as the impact of uncertainties thereupon. This work presents a rigorous methodology for the optimal design and control under uncertainty of reactive distillation. The methodology can also be used to identify and investigate mitigation strategies for process failures arising due to design and/or operation deficiencies under changed processing conditions, based on the evaluation of different design and/or control alternatives. The first step of the methodology is the simultaneous (MINLP) optimisation of the design and operation of a reactive distillation process superstructure, used to explore the possible steady-state design alternatives available, including ancillary equipment such as pre- and side-reactors, side-strippers and additional distillation columns, based on product-related constraints and a detailed objective cost function. The next step is the investigation of the dynamic control performance of this optimal system, where conventional and advanced process control strategies are considered in order to investigate how robust the system is towards operational disturbances, or whether revising the optimal steady-state design is required. As the optimisation depends heavily on accurate data for reaction kinetics and separation performance, the final step of the methodology is the evaluation of the impact of parameter uncertainty on the performance of the optimal controlled system, including redesigning the controlled system if required. The methodology is demonstrated using a number of industrially relevant case studies with different reaction and separation characteristics in order to investigate how these determine the design and control of an economically attractive and rigorous reactive distillation process. It is demonstrated that the process characteristics have a significant impact on the design of the system, and that auxiliary equipment may be required to meet production specifications and/or to ensure robust controlled behaviour. It is also shown that, under parameter uncertainty, an optimal controlled system may nevertheless face performance issues, and revising the design and/or operation of the process may be required in order to mitigate such situations

Modeling and Control of Post-Combustion CO2 Capture Process Integrated with a 550MWe Supercritical Coal-fired Power Plant

This work focuses on the development of both steady-state and dynamic models for an monoethanolamine (MEA)-based CO2 capture process for a commercial-scale supercritical pulverized coal (PC) power plant, using Aspen PlusRTM and Aspen Plus DynamicsRTM. The dynamic model also facilitates the design of controllers for both traditional proportional-integral-derivative (PID) and advanced controllers, such as linear model predictive control (LMPC), nonlinear model predictive control (NMPC) and H? robust control.;A steady-state MEA-based CO2 capture process is developed in Aspen PlusRTM. The key process units, CO2 absorber and stripper columns, are simulated using the rate-based method. The steady-state simulation results are validated using experimental data from a CO2 capture pilot plant. The process parameters are optimized with the goal of minimizing the energy penalty. Subsequently, the optimized rate-based, steady-state model with appropriate modifications, such as the inclusion of the size and metal mass of the equipment, is exported into Aspen Plus DynamicsRTM to study transient characteristics and to design the control system. Since Aspen Plus DynamicsRTM does not support the rate-based model, modifications to the Murphree efficiencies in the columns and a rigorous pressure drop calculation method are implemented in the dynamic model to ensure consistency between the design and off-design results from the steady-state and dynamic models. The results from the steady-state model indicate that between three and six parallel trains of CO2 capture processes are required to capture 90% CO2 from a 550MWe supercritical PC plant depending on the maximum column diameter used and the approach to flooding at the design condition. However, in this work, only two parallel trains of CO2 capture process are modeled and integrated with a 550MWe post-combustion, supercritical PC plant in the dynamic simulation due to the high calculation expense of simulating more than two trains.;In the control studies, the performance of PID-based, LMPC-based, and NMPC-based approaches are evaluated for maintaining the overall CO2 capture rate and the CO2 stripper reboiler temperature at the desired level in the face of typical input and output disturbances in flue gas flow rate and composition as well as change in the power plant load and variable CO2 capture rate. Scenarios considered include cases using different efficiencies to mimic different conditions between parallel trains in real industrial processes. MPC-based approaches are found to provide superior performance compared to a PID-based one. Especially for parallel trains of CO2 capture processes, the advantage of MPC is observed as the overall extent of CO2 capture for the process is maintained by adjusting the extent of capture for each train based on the absorber efficiencies. The NMPC-based approach is preferred since the optimization problem that must be solved for model predictive control of CO2 capture process is highly nonlinear due to tight performance specifications, environmental and safety constraints, and inherent nonlinearity in the chemical process. In addition, model uncertainties are unavoidable in real industrial processes and can affect the plant performance. Therefore, a robust controller is designed for the CO2 capture process based on ?-synthesis with a DK-iteration algorithm. Effects of uncertainties due to measurement noise and model mismatches are evaluated for both the NMPC and robust controller. The simulation results show that the tradeoff between the fast tracking performance of the NMPC and the superior robust performance of the robust controller must be considered while designing the control system for the CO2 capture units. Different flooding control strategies for the situation when the flue gas flow rate increases are also covered in this work

Simulation Study on Operations Aspects of a Reactive Distillation Column for Production ofEthyl Acetate Using ASPEN PLUS™ and ASPEN DYNAMIC™

Ethyl acetate is a widely used organic compound in manufacturing of printing inks, paints, coatings, perfume, film, food additives, pharmaceutical and others due to its low boiling point. There were numerous research carried out in different areas related with ethyl acetate production. In recent years, due to the increasing trend in ethyl acetate demand, reactive distillation that combined reaction process and distillation process technique has been used for ethyl acetate production studies. However, most of the researchers focus on column configuration and control of the column. There are limited studies being carried out on starting up a reactive distillation column in dynamic simulation

Integration of Process Design, Scheduling, and Control Via Model Based Multiparametric Programming

The conventional approach to assess the multiscale operational activities sequentially often leads to suboptimal solutions and even interruptions in the manufacturing process due to the inherent differences in the objectives of the individual constituent problems. In this work, integration of the traditionally isolated process design, scheduling, and control problems is investigated by introducing a multiparametric programming-based framework, where all decision layers are based on a single high fidelity model. The overall problem is dissected into two constituent parts, namely (i) design and control, and (ii) scheduling and control problems. The proposed framework was first assessed on these constituent subproblems, followed by the implementation on the overall problem. The fundamental steps of the framework consists of (i) developing design dependent offline control and scheduling strategies, and (ii) exact implementation of these offline rolling horizon strategies in a mixed-integer dynamic optimization problem for the optimal design. The design dependence of the offline operational strategies allows for the integrated problem to consider the design, scheduling, and control problems simultaneously. The proposed framework is showcased on (i) a binary distillation column for the separation of toluene and benzene, (ii) a system of two continuous stirred tank reactor, (iii) a small residential heat and power network, and (iv) two batch reactor systems. Furthermore, a novel algorithm for large scale multiparametric programming problems is proposed to solve the classes of problems frequently encountered as a result of the integration of rolling horizon strategies

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors