Sergio Ferrer-Nadal

Modelling and simulation of a tyre gasification plant for synthesis gas production

Abstract Gasification is becoming one of the best alternatives for waste solids reuse, especially for those, as tyres, which can cause a significant environmental impact. The proposed gasification model improves the understanding of the process and can be used as a predictive tool at the optimization stage. Validation of this model is carried out using the gasification pilot plant located at the Chemical Engineering Department of Universitat Politecnica de Catalunya (UPC)

A novel continuous-time MILP approach for short-term scheduling of multipurpose pipeless batch plants

Abstract This work presents an alternative MILP mathematical formulation for the short-term scheduling of pipeless batch plants based on a continuous-time representation that relies on the general precedence notion. Besides of the intrinsic characteristics of a pipeless plant, this model considers the moveable vessels as an additional resource that has to be allocated and sequenced. This fact allows proposing a strategy consisting of a sequential treatment of the resources to reduce the complexity of the problem.

Enhanced modeling and integrated simulation of gasification and purification gas units targeted to clean power production

This work presents a structured and validated conceptual model of an integrated gasification combined cycle (IGCC) power plant. A pressurized entrained flow (PRENFLO) gasifier, subsequent gas cleaning operations for fly ashes, ammonia, and sulfur compounds removal, heat recovery steam generator (HRSG) and combined cycle unit operations have been modeled in steady state. The model has been developed using Aspen Hysys® and Aspen Plus®. Parts of it have been developed in Matlab, which is mainly used for artificial neural network (ANN) training and parameters estimation Predicted results of clean gas composition and generated power present a good agreement with industrial data. This study is aimed at obtaining a support tool for optimal solutions assessment of different gasification plant configurations, under different input data sets.

An integrated framework for on-line supervised optimization

Abstract In this paper, an integrated supervisory framework is envisaged for more robust on-line optimization and exception handling. This system takes advantage of the capabilities of real time evolution algorithm [Sequeira, S., Graells, M., Puigjaner, L. (2002). Real time evolution for Online Optimization of Continuous Processes. Industrial & Engineering Chemistry Research 41, 1815–1825] plus a fault diagnosis system (FDS) for managing abnormal situations. Both systems are part of a supervisory module which is responsible for handling plant incidences (faults and disturbances) by taking the appropriate corrective actions. Thus, a more satisfactory on-line performance is achieved, while reaction to incidence is enhanced by providing combined cause-effect information to plant managers. The implementation of the supervised real time evolution scheme has been performed using Matlab and the commercial simulation package HYSYS.Plant, taking advantage of their communication capabilities (COM technology). The developed fault diagnosis system comprises a detection module based on multivariate statistical techniques and an isolation module that uses an artificial neural network as a pattern classifier. The benefits of this framework are illustrated through a case study consisting in a debutanizer distillation column. The handling of various plant incidences, involving different sources and types of disturbances are considered. Finally, results of the supervised real time evolution (SRTE) are compared with those obtained using the standard real time optimization approach (RTO), showing superior performance in most of the cases.

A mathematical programming approach including flexible recipes to batch operation rescheduling

Abstract The inherent dynamic nature of industrial environments often needs not only the execution of the required rescheduling actions but also the proper adjustment of the production recipe to the current process conditions. Therefore, the concept of flexible recipe becomes an important part of the rescheduling framework that allows full exploitation of the batch plant intrinsic flexibility. This work introduces a rigorous mathematical approach that incorporates the concept of recipe flexibility to batch operation rescheduling.

On-line fault diagnosis support for real time evolution applied to multi-component distillation

Abstract In this paper, the Real Time Evolution algorithm (Sequeira et al., 2002) is applied to the on-line optimization of a debutanizer distillation column. A fault diagnosis system (FDS) implemented within a supervisory module is responsible for handling incidences (faults and disturbances) happening in the plant by taking the appropriate corrective actions, including the activation of the RTE system. Thus, a more robust on-line performance is achieved. The implementation of the RTE scheme has been performed using Matlab© and the commercial simulation package HYSYS.Plant©, taking advantage of their communication capabilities (COM technology). Different possible plant incidences are addressed, involving different sources and types of disturbances. Results of RTE are compared with those obtained using the standard Real Time Optimization approach, showing better performance in most of the cases.

Rigorous scheduling resolution of complex multipurpose batch plants: S-Graph vs. MILP

Abstract Highly efficient optimization methods are needed to deal rigorously with complex short-term production scheduling problems. This work presents a comparative study of two alternative solution strategies based on a mathematical programming approach and a graph-oriented method. Different scenarios of increasing complexity are addressed in order to evaluate the current capabilities and limitations of each solution method.

Exploiting the use of a flexible recipe framework to manage financial risk

Abstract This work explores the use of the flexible recipe framework as a manner to manage the risk associated with the operation of batch chemical plants under uncertain market trends. The scheduling problem under uncertainty is mathematically formulated as a multi-objective mixed integer linear problem accounting for the maximization of the expected profit and minimization of risk. A decomposition strategy based on the Sample Average Approximation (SAA) is applied to overcome the numerical difficulties associated with such mathematical formulation.