Nicolás J. Scenna

An improved piecewise outer-approximation algorithm for the global optimization of MINLP models involving concave and bilinear terms

In this paper a new version of the Outer Approximation for Global Optimization Algorithm by Bergamini et al. [Bergamini, M.L., Aguirre, P., & Grossmann, I.E. (2005a). Logic based outer approximation for global optimization of synthesis of process networks. Computers and Chemical Engineering 29, 1914] is proposed, in order to speed up the convergence in nonconvex MINLP models that involve bilinear and concave terms. Bounding problems are constructed replacing these nonconvex terms by piecewise linear underestimators. These problems, which correspond to mixed-integer linear programs, are solved to generate approximate solutions with improved objective value. When no further feasible solution can be found, this guarantees that the upper bound cannot be improved in the nonconvex problem, thus providing a termination criterion. The new algorithm is applied to five different synthesis problems in the areas of water networks, heat exchanger networks and distillation sequences. The results show a significant reduction in the computational cost compared with the previous version of the algorithm.

Fault diagnosis, direct graphs, and fuzzy logic

Abstract Fault diagnosis is the problem of finding out the root cause (the fault) of process malfunctions. In this work we present a new model based approach procedure for fault diagnosis in conventional chemical processes. The process model is a Signed Directed Graph (SDG). The SDG is used by a Qualitative Simulator. This stage allows us to know, for each potential fault, the possible process behaviour. This information is compiled into a set of IF-THEN rules. An Expert System evaluates them using information about the actual process state. Fuzzy Logic is used in this evaluation. By the way, the fault whose rule has the highest value of certainty should be the first one considered by the operator. Finally, the additional information provided by the Qualitative Simulation enables the Expert System to explain the diagnostic. Besides, this additional information can be used to improve the diagnostic.

Some aspects of fault diagnosis in batch processes

Abstract The aim of this paper is to explore alternatives for implementing Fault Diagnosis tools for complex dynamic systems, like complete batch processes. A hybrid, modular, hierarchical architecture is proposed. It is based on a temporal division of the process evolution and the task sequences.

A dynamic simulator for MSF plants

Abstract This work presents a dynamic simulator for MSF desalination plants. It takes into account the heaters and stages dynamic, hydraulic, standard instrumentation and control systems. This simulator was developed to study the effects of faults that may affect a MSF system. In order to extend the results scope, the simulator allows the modification of MSF topology and parameters into a wide range. Indeed, it is possible to change the number of stages belonging to the recovery and rejection sections, controller parameters (set point, integral time and gain), valve size, pump characteristics, seawater conditions, stages and heater dimensions, etc. Since fault simulation is the main simulator goal, the model and its resolution were carefully designed to enhance stability and speed. The user can select the fault to simulate among a set of possible faults (fault in controllers, sensors, pumps, etc.), and can specify the activation time (at which the fault starts), the development time (time elapsed from the fault start up until the fault reaches its maximum magnitude) and the fault magnitude. Thus, it is possible simulate step and ramp perturbations. The simulator was tested with data from real plants and it has shown a good performance. To make the simulator operation easier, it was developed by using a visual language for Windows 95.

Fault diagnosis for a MSF using a SDG and fuzzy logic

Abstract This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) desalination plant MSF using a real time expert system. This diagnostic system processes the plant data to determine whether the process state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal state. The first step is to determinate the potential faults. This set contains all the faults the diagnostic system should be able to recognize. Then, to improve the diagnostic system performance, a careful selection of the plant sensors that will be supervised by the diagnostic system is done. The knowledge base of the expert system is automatically obtained from a qualitative model of the plant. The qualitative model is a signed directed graph (SDG). The SDG is used by a qualitative simulator to forecast, for each potential fault, the possible qualitative evolutions of the plant. This information is used to generate rules ‘if-then’ to build the knowledge base. During the diagnostic system operation, at each sampling time, the readings of the previously selected sensors are transformed in qualitative values. These values are used by the expert system to evaluate the rules by using fuzzy logic. The result is an index between 0 and 1 for each potential fault. This number represents the certainty about the corresponding fault is affecting the plant. The higher is the value, the higher is the certainty of that affirmation. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.

Optimal MSF plant design

The aim of this paper is to present a rigorous model for Multi-Stage Flash Evaporation System (MSF). The MSF system is represented as a No Linear Programming (NLP) model. The model incorporates a high number of non-linear restrictions; so the achievement of the global optimum is difficult. Here, the study of an algorithm for the system optimisation is presented.

A methodology for fault diagnosis in large chemical processes and an application to a multistage flash desalination process: Part I

Abstract This work presents a new strategy for fault diagnosis in large chemical processes (E.E. Tarifa, Fault diagnosis in complex chemistries plants: plants of large dimensions and batch processes. Ph.D. thesis, Universidad Nacional del Litoral, Santa Fe, 1995). A special decomposition of the plant is made in sectors. Afterwards each sector is studied independently. These steps are carried out in the off-line mode. They produced vital information for the diagnosis system. This system works in the on-line mode and is based on a two-tier strategy. When a fault is produced, the upper level identifies the faulty sector. Then, the lower level carries out an in-depth study that focuses only on the critical sectors to identify the fault. The loss of information produced by the process partition may cause spurious diagnosis. This problem is overcome at the second level using qualitative simulation and fuzzy logic. In the second part of this work, the new methodology is tested to evaluate its performance in practical cases. A multiple stage flash desalination system (MSF) is chosen because it is a complex system, with many recycles and variables to be supervised. The steps for the knowledge base generation and all the blocks included in the diagnosis system are analyzed. Evaluation of the diagnosis performance is carried out using a rigorous dynamic simulator.

Fault diagnosis for a MSF using neural networks

Abstract This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF) desalination plant using artificial neural networks (ANNs). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal process state. The diagnostic system has an ANN for each potential fault. Every ANN processes the plant data looking for symptoms of their respective faults. At a given time, the result reported by an ANN is an index between 0 and 1. This number represents the certainty about the corresponding fault is affecting the plant. The higher is the value, the higher is the certainty of the affirmation. The structure of each ANN is simpler than those reported in the bibliography; however, the performance is better. These results are obtained due to a careful selection of the diagnostic system output and the use of a special training method. That training method calculates an appropriate value for the output of each ANN instead of setting it at 0 or 1 only. The new value of the output does not depend on the fault that causes the inputs but it does only on the degree of matching between the observed evolution and the expected one for the fault corresponding to each ANN. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.

Graphical procedure for reactive distillation systems

In this paper we analyze a ternary reactive distillation system, where an equilibrium chemical reaction occurs in the liquid phase. By using a set of transformed variables proposed by Barbosa el al. (1988b) and well known graphical procedures for non-reactive systems; the minimum reflux ratio, minimum number of equilibrium stages, mass and energy balances for a reactive column and flash can be easily obtained. The procedures developed in this paper are applied to ISOBUTYLENE-METHANOL-MTBE system. The main objective in developing this model is to obtain, as much as possible, rigorous information for the analysis of one reactive distillation column in a Process Simulator with capability of handling this type of new operation.

Global Optimal Design of Mechanical Vapor Compression (MVC) Desalination Process

Abstract This paper deals with the optimal design of Mechanical Vapor Compression (MVC) desalination process. Precisely, a detailed mathematical model of the process and a deterministic global optimization algorithm are applied to determine the optimal design and operating conditions for the system. The resulting model involves the real-physical constraints for the evaporation process. Nonlinear equations in terms of chemical-physical properties and design equations (efficiencies, Non-Allowance Equilibrium, Boiling Point Elevation, heat transfer coefficients, momentum balances, among others) are used to model the process. The model has been solved by using a deterministic global optimization algorithm previously developed by the authors [7] and implemented in a General Algebraic Modeling System GAMS [1]. The generalized reduced gradient algorithm CONOPT 2.041 [2] is used as NLP local solver. The model was successfully solved for different seawater conditions (salinity and temperature) and fresh water production levels. The influence of the production requirements on the process efficiency as well as the algorithms performance is presented.