A Multivariable Approach for Control System Optimization of IGCC with CCS in DECAR Bit Project

Abstract IGCCs with CCS differ from existing IGCCs mainly because of steam integration between gasification process and combined cycle, and because of selective capture of CO2. A dynamic simulator of IGCCs with CCS considered in DECARBit project was developed by using a in house code, ALTERLEGO, and a commercial code ASPEN HYSYS ® . Simulators were used to assess flexibility of the process design and effectiveness of the control system during load changes. Starting from steady state results at nominal load, the simulator development has been implemented to assure a stable transient behavior during load reduction. As a result of this study, the flue-gas temperature and IP pressure should be regulated at fixed setpoint. Moreover, critical behavior of CO shift temperature controllers,can be mitigated by means of suitable setpoint coordination

Application of a method to diagnose the source of performance degradation in MPC systems

Model Predictive Control systems may suffer from performance degradation mainly for two reasons: (i) external unmeasured disturbances are not estimated correctly, (ii) the (linear) dynamic model used by the MPC does not match (any longer) the actual process response. In this work we present the application of a method to detect when performance is not optimal, to diagnose the source of performance degradation and to propose appropriate corrections. In the simplest situation (i), optimal performance can be restored by recomputing the estimator parameters; in the other case (ii), re-identification becomes necessary. The method is based on analysis of the prediction error, i.e. the difference between the actual measured output and the corresponding model prediction, and uses three main tools: a statistical (whiteness) test on the prediction error sequence, a subspace identification method to detect the order of the input-to-prediction error system, and a nonlinear optimization algorithm to recompute optimal estimator parameters. We illustrate the effectiveness of the method on a large-scale rigorously simulated industrial process. Copyright © 2013, AIDIC Servizi S.r.l

MPC performance monitoring of a rigorously simulated industrial process

We address in this paper the application of a recently proposed MPC performance monitoring method to a rigorously simulated industrial process. The methodology aims at detecting possible sources of suboptimal performance of linear offset-free MPC algorithms by analysis of the prediction error sequence, discriminating between the presence of plant/model mismatch and incorrect disturbance/state estimation, and proposing for each scenario an appropriate corrective action. We focus on the applicability of the method to large-scale industrial systems, which typically comprise a block structure, devising efficient and scalable diagnosis and correction procedures. We also discuss and support the application of this method when the controlled plant shows a mild nonlinear behavior mainly associated with operating point changes. A high-fidelity dynamic simulation model of a crude distillation unit was developed in UniSim® Design and used as representative test bench. Results show the efficacy of the method and indicate possible research directions for further improvements. © IFAC

Fungal Biosorption, An Innovative Treatment for the Decolourisation and Detoxification of Textile Effluents

Textile effluents are among the most difficult-to-treat wastewaters, due to their considerable amount of recalcitrant and toxic substances. Fungal biosorption is viewed as a valuable additional treatment for removing pollutants from textile wastewaters. In this study the efficiency of Cunninghamella elegans biomass in terms of contaminants, COD and toxicity reduction was tested against textile effluents sampled in different points of wastewater treatment plants. The results showed that C. elegans is a promising candidate for the decolourisation and detoxification of textile wastewaters and its versatility makes it very competitive compared with conventional sorbents adopted in industrial processes

Data-driven Models for Advanced Control of Acid Gas Treatment in Waste-to-energy Plants

This paper presents a study of identification and validation of data-driven models for the description of the acid gas treatment process, a key step of flue gas cleaning in waste-to-energy plants. The acid gas removal line of an Italian plant, based on the injection of hydrated lime, Ca(OH)2, for the abatement of hydrogen chloride, HCl, is investigated. The final goal is to minimize the feed rate of reactant needed to achieve the required HCl removal performance, also reducing as a consequence the production of solid process residues. Process data are collected during dedicated plant tests carried out by imposing Generalized Binary Noise (GBN) sequences to the flow rate of Ca(OH)2. Various input-output and state-space models are identified with success, and related model orders are optimized. The models are then validated on different datasets of routine plant operation. The proposed modeling approach appears reliable and promising for control purposes, once implemented into advanced model-based control structures

How auxiliary variables and plant data collection affect closed-loop performance of inferential control

The design of property estimators for inferential control is addressed in this paper, and the effects of the auxiliary variables (estimator's inputs) and of the approach to collect plant data, used to compute the model coefficients, are investigated. The concept of steady-state closed-loop consistency, which is the ability of an estimator to guarantee low offset in the unmeasured controlled variables, is adopted and theoretical results about this property are derived. It is shown how the selection of auxiliary variables represents the most crucial design step that determines the final closed-loop performance of an inferential control system. When this selection is done on a steady-state closed-loop consistency basis, the closed-loop performance is satisfactory, and it is secondary how the dataset is built. On the other hand, when "inconsistent" inputs are used, the performance is, in general, poor and may be significantly affected (in positive or in negative) by the dataset characteristics. © 2007 Elsevier Ltd. All rights reserved

Comparison of input signals in subspace identification of multivariable ill-conditioned systems

Ill-conditioned processes often produce data of low quality for model identification in general, and for subspace identification in particular, because data vectors of different outputs are typically close to collinearity, being aligned in the "strong" direction. One of the solutions suggested in the literature is the use of appropriate input signals, usually called "rotated" inputs, which must excite sufficiently the process in the "weak" direction. In this paper open-loop (uncorrelated and rotated) random signals are compared against inputs generated in closed-loop operation, with the aim of finding the most appropriate ones to be used in multivariable subspace identification of ill-conditioned processes. Two multivariable ill-conditioned processes are investigated and as a result it is found that closed-loop identification gives superior models, both in the sense of lower error in the frequency response and in terms of higher performance when used to build a model predictive control system. © 2007 Elsevier Ltd. All rights reserved