Silvana Revollar

Integrated design and control of chemical processes – Part I: Revision and classification

Abstract This work presents a comprehensive classification of the different methods and procedures for integrated synthesis, design and control of chemical processes, based on a wide revision of recent literature. This classification fundamentally differentiates between “projecting methods”, where controllability is monitored during the process design to predict the trade-offs between design and control, and the “integrated-optimization methods” which solve the process design and the control-systems design at once within an optimization framework. The latter are revised categorizing them according to the methods to evaluate controllability and other related properties, the scope of the design problem, the treatment of uncertainties and perturbations, and finally, the type the optimization problem formulation and the methods for its resolution.

Integration of set point optimization techniques into nonlinear MPC for improving the operation of WWTPs

Abstract Optimization and control strategies are necessary to keep wastewater treatment plants (WWTPs) operating in the best possible conditions, maximizing effluent quality with the minimum consumption of energy. In this work, a benchmarking of different hierarchical control structures for WWTPs that combines static and dynamic real time optimization (RTO) and nonlinear model predictive control (NMPC) is presented. The objective is to evaluate the enhancement of the operation in terms of economics and effluent quality that can be achieved when introducing NMPC technologies in the distinct levels of the multilayer structure. Three multilayer hierarchical structures are evaluated and compared for the N-Removal process considering the short term and long term operation in a rain weather scenario. A reduction in the operation costs of approximately 20% with a satisfactory compromise to effluent quality is achieved with the application of these control schemes.

Metodología para la Obtención de Modelos Semifísicos de Base Fenomenológica Aplicada a una Sulfitadora de Jugo de Caña de Azúcar

This work presents the Phenomenological Based Semi-physical Model (PBSM) as a useful tool for the design, control and optimization of chemical and bio-technology processes. First, a detailed description of a methodology to obtain a PBSM based on the knowledge of the basic principles of the process: mass, energy and momentum conservation, as well as gradient principle for obtaining model constitutive equations. The described methodology is applied to the clarification stage of a sugar cane refining plant. Specifically, the sulfitation tower is modelled step by step. The model is simulated and validated by comparing its behaviour with data taken from a real sulfitation tower.

Integrated design and control of chemical processes : Part II: an illustrative example

Abstract In this paper, several methodologies of integrated design are proposed and applied to the design of wastewater treatment plants and their control system, focusing on the activated sludge process, within a novel multiobjective framework. The scope of the problem considers both fixed plant layout and plant structure selection by defining a simple superstructure. The control strategy chosen is a linear Model Predictive Controller (MPC) with terminal penalty. The evaluation of the controllability has been performed using norm based indexes, and the robustness conditions for different uncertainty sources have been considered, in the frequency and time domains. The optimization strategies used are based on the integration of stochastic and deterministic methods, as well as genetic algorithms. The presented methodologies and their application to wastewater treatment plants can be considered as an illustrative example in the universe of integrated design techniques presented in the Part I article of this series.

Algorithmic synthesis and integrated design for activated sludge processes using genetic algorithms

Abstract This work presents an approach for the Synthesis and Integrated Design of an activated sludge process. The mathematical formulation translates a superstructure that contains all the design alternatives into a mixed-integer dynamical optimization problem with non-linear constraints. A real-coded genetic algorithm is proposed for the solution of such complex problem as an alternative to classical optimisation techniques. Thus, the process synthesis considering open-loop-dynamical-performance indexes and also the closed loop integrated design for the plant are carried out. The results are encouraging for future application of these techniques to solve process synthesis problems

Model Predictive Control of BSM1 benchmark of wastewater treatment process: A tuning procedure

The BSM1 (Benchmark Simulation Model N° 1) has become the standard simulation tool for performance assessment of control techniques applied to wastewater treatment plants (WWTP). Control of WWTP is not trivial because of large disturbances in the influent, nonlinearities, delays, and interaction between variables. In this work a multivariable Model Predictive Controller (MPC) is implemented and optimally tuned. The paper presents results for different tuning situations without the use of long time consuming simulations and considering uncertainty by means of multiple linearized models. Control of the dissolved oxygen in the last aerated reactor and nitrate level in the last anoxic compartment has been tested in simulations using the MPC control strategy. The obtained results show the MPC benefits when it is properly tuned.

Diseño Simultáneo de Proceso y Control de una Torre Sulfitadora de Jugo de Caña de Azúcar

This work focuses in the design and control of a plant used in the clarification stage of the sugar cane juice refining process. An innovative approach to the simultaneous design and control problem is presented, which considers the state controllability (based on practical controllability metrics) and the output controllability (based on dynamical performance indices) using as example the sulphitation tower. This statement translates into a non linear optimization problem where constraints are imposed over the plant operating conditions, the state controllability metrics and some closed loop performance indices while the investment, operating and control costs are minimized. The optimization problem was solved successfully using genetic algorithms.

Economic optimization of Wastewater Treatment Plants using Non Linear Model Predictive Control

Operation of Wastewater Treatment Plants (WWTP) is a really challenging problem from the point of view of being an industry process that does not deliver any turnover. Instead, the costs of operation are always to be faced against the environmental benefits. It is from this point of view that any control and operation formulation that includes economic considerations may provide useful perspectives on this respect. This work proposes the implementation of Dynamic Real Time Optimization (D-RTO) integrating the economic optimization into Non-linear Model Predictive Control (NMPC) techniques for improving the operation of WWTPs.

Genetic Algorithms for the Synthesis and Integrated Design of Processes Using Advanced Control Strategies

This work presents a real-coded genetic algorithm to perform the synthesis and integrated design of an activated sludge process using and advanced Multivariable Model-based Predictive Controller (MPC). The process synthesis and design are carried out simultaneously with the MPC tuning to obtain the most economical plant which satisfies the controllability indices that measure the control performance (H∞ and l1 norms of different sensitivity functions of the system). The mathematical formulation results into a mixed-integer optimization problem with non-linear constraints. The quality of the solutions obtained evidence that real-coded genetic algorithms are a valid and practical alternative to deterministic optimization methods for such complex problems.

An activated-sludge-process application of integrated design and predictive control with instantaneous linearization

In this work the integrated design of the activated sludge process is addressed. The objective is to determine the best plant parameters and working point that minimize the operation costs related to the Effluent Quality and the Aeration Energy, while imposing constraints on the plant condition number (γ) and the perturbation condition number (γp), ensuring open-loop controllability. A linear multivariable predictive control (MPC) is used for the closed loop design, and it is used also a very practical non-linear version of the MPC, based on the instantaneous linearization of non linear models of the plant (the phenomenological model as well as a neural network model obtained by identification). The results are analyzed based on two aspects: the improved performance of the controlled system when using the integrated design instead of a classical economic design, and the convenience of the instantaneous linearization to realize a non-linear MPC.