Carmen G. Moles

Improving food processing using modern optimization methods

In this contribution, computer-aided optimization is presented as the ultimate tool to improve food processing. The state of the art is reviewed, especially focusing in recent developments using modern optimization techniques. Their potential for industrial applications is also discussed in the light of several important examples. Finally, future trends and research needs are outlined.

Global Optimization of Bioprocesses using Stochastic and Hybrid Methods

In this contribution, we will focus on problems arising in the context of biochemical process engineering. Many of these problems can be stated as the optimization of non-linear dynamic systems. Relevant classes in this domain are (i) optimal control problems (dynamic optimization), (ii) inverse problems (parameter estimation), and (iii) simultaneous design and control optimization problems. Most of these problems are, or can be transformed to, nonlinear programming problems subject to differential-algebraic constraints. It should be noted that their highly constrained and non-linear nature often causes non-convexity, thus global optimization methods are needed to find suitable solutions.

Solving nonconvex climate control problems: pitfalls and algorithm performances

Abstract Global optimization can be used as the main component for reliable decision support systems. In this contribution, we explore numerical solution techniques for nonconvex and nondifferentiable economic optimal growth models. As an illustrative example, we consider the optimal control problem of choosing the optimal greenhouse gas emissions abatement to avoid or delay abrupt and irreversible climate damages. We analyze a number of selected global optimization methods, including adaptive stochastic methods, evolutionary computation methods and deterministic/hybrid techniques. Differential evolution (DE) and one type of evolution strategy (SRES) arrived to the best results in terms of objective function, with SRES showing the best convergence speed. Other simple adaptive stochastic techniques were faster than those methods in obtaining a local optimum close to the global solution, but mis-converged ultimately.

Integrated Process Design and Control Via Global Optimization: A Wastewater Treatment Plant Case Study

The problem of simultaneous design and control optimization is discussed in this contribution. We consider a NLP-DAEs formulation of this problem, and we show how it can be solved using stochastic global optimization (GO) methods. In particular, we consider a medium complexity case study related with the integrated process design and control of a wastewater treatment plant. Parallel versions of several of the GO solvers are also presented which can handle larger problems in modest computation times.

Integrated Process Design and Control Via Global Optimization

The problem of simultaneous design and control optimization is discussed in this contribution. We consider a NLP-DAEs formulation of this problem, and we show how it can be solved using stochastic global optimization (GO) methods. In particular, we consider a medium complexity case study related with the integrated process design and control of a wastewater treatment plant. Parallel versions of several of the GO solvers are also presented which can handle larger problems in modest computation times.

Global optimization of climate control problems using evolutionary and stochastic algorithms

Global optimization can be used as the main component for reliable decision support systems. In this contribution, we explore numerical solution techniques for nonconvex and nondifferentiable economic optimal growth models. As an illustrative example, we consider the optimal control problem of choosing the optimal greenhouse gas emissions abatement to avoid or delay abrupt and irreversible climate damages. We analyze a number of selected global optimization methods, including adaptive stochastic methods, evolutionary computation methods and deterministic/hybrid techniques.

Integrated process design and control via global optimization: A wastewater treatment plant case study

The problem of simultaneous design and control optimization is discussed in this contribution. We consider a NLP-DAEs formulation of this problem, and we show how it can be solved using stochastic global optimization (GO) methods. In particular, we consider a medium complexity case study related with the integrated process design and control of a wastewater treatment plant. Parallel versions of several of the GO solvers are also presented which can handle larger problems in modest computation times.

Optimal Non-Linear Design and Control of Bioprocesses via Linear Programming

Abstract An optimization approach for bioprocesses based on linear programming is developed and applied to a wastewater treatment plant. After defining an objective function reflecting both investment costs and “paracosts” (stability, flexibility and controllability), we define a set of constraints determined by the system components and technical and economical factors. Results obtained reveals that significant improvements in controllability and cost reduction are achieved. We conclude that the incorporation of this method to a dynamic process simulator would lead to a fast and interactive tool for obtaining near-optimal integrated designs for bioprocess plants.

Nonlinear Multi-Criteria Optimization for the Integrated Design and Control of Bioprocesses

Abstract The simultaneous design and control of continuous bioprocesses is considered here as a multi-objective optimization problem subject to non-linear differential-algebraic constraints. This type of problem is very challenging to solve due to its non-convexity (multi-modality). We present two novel solution approaches based on extensions of an stochastic global optimization method. The robustness and efficiency of these approaches are illustrated with a wastewater treatment plant case study.