602 research outputs found
Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant
The energy efficiency of industrial plants is an important issue in any type of business but particularly in
the chemical industry. Not only is it important in order to reduce costs, but also it is necessary even more
as a means of reducing the amount of fuel that gets wasted, thereby improving productivity, ensuring
better product quality, and generally increasing profits. This article describes a decision system developed
for optimizing the energy efficiency of a petrochemical plant. The system has been developed after
a data mining process of the parameters registered in the past. The designed system carries out an optimization
process of the energy efficiency of the plant based on a combined algorithm that uses the following
for obtaining a solution: On the one hand, the energy efficiency of the operation points occurred in
the past and, on the other hand, a module of two neural networks to obtain new interpolated operation
points. Besides, the work includes a previous discriminant analysis of the variables of the plant in order to
select the parameters most important in the plant and to study the behavior of the energy efficiency
index. This study also helped ensure an optimal training of the neural networks. The robustness of the
system as well as its satisfactory results in the testing process (an average rise in the energy efficiency
of around 7%, reaching, in some cases, up to 45%) have encouraged a consulting company (ALIATIS) to
implement and to integrate the decision system as a pilot software in an SCADA
An Expert System to Improve the Energy Efficiency of the Reaction Zone of a Petrochemical Plant
Energy is the most important cost factor in the petrochemical industry.
Thus, energy efficiency improvement is an important way to reduce these
costs and to increase predictable earnings, especially in times of high energy
price volatility. This work describes the development of an expert system for
the improvement of this efficiency of the reaction zone of a petrochemical
plant. This system has been developed after a data mining process of the variables
registered in the plant. Besides, a kernel of neural networks has been
embedded in the expert system. A graphical environment integrating the proposed
system was developed in order to test the system. With the application of
the expert system, the energy saving on the applied zone would have been about
20%.Junta de Andalucía TIC-570
Energy efficient control and optimisation techniques for distillation processes
PhD ThesisDistillation unit is one of the most energy intensive processes and is among the major CO2 emitter in the chemical and petrochemical industries. In the quest to reduce the energy consumption and hence the environmental implications of unutilised energy, there is a strong motivation for energy saving procedures for conventional columns. Several attempts have been made to redesign and heat integrate distillation column with the aim of reducing the energy consumption of the column. Most of these attempts often involve additional capital costs in implementing. Also a number of works on applying the second law of thermodynamics to distillation column are focused on quantifying the efficiency of the column. This research aims at developing techniques of increasing the energy efficiency of the distillation column with the application of second law using the tools of advanced control and optimisation. Rigorous model from the fundamental equations and data driven models using Artificial neural network (ANN) and numerical methods (PLS, PCR, MLR) of a number of distillation columns are developed. The data for the data driven models are generated from HYSYS simulation. This research presents techniques for selecting energy efficient control structure for distillation processes. Relative gain array (RGA) and relative exergy array (REA ) were used in the selection of appropriate distillation control structures. The viability of the selected control scheme in the steady state is further validated by the dynamic simulation in responses to various process disturbances and operating condition changes. The technique is demonstrated on two binary distillation systems. In addition, presented in this thesis is optimisation procedures based on second law analysis aimed at minimising the inefficiencies of the columns without compromising the qualities of the products. ANN and Bootstrap aggregated neural network (BANN) models of exergy efficiency were developed. BANN enhances model prediction accuracy and also provides model prediction confidence bounds. The objective of the optimisation is to maximise the exergy efficiency of the column. To improve the reliability of the optimisation strategy, a modified objective function incorporating model prediction confidence bounds was presented. Multiobjective optimisation was also explored. Product quality constraints introduce a measure of penalization on the optimisation result to give as close as possible to what obtains in reality. The optimisation strategies developed were applied to binary systems, multicomponents system, and crude distillation system. The crude distillation system was fully explored with emphasis on the preflash unit, atmospheric distillation system (ADU) and vacuum distillation system (VDU). This study shows that BANN models result in greater model accuracy and more robust models. The proposed
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techniques also significantly improve the second law efficiency of the system with an additional economic advantage. The method can aid in the operation and design of energy efficient column.Commonwealth scholarship commissio
Distillation
The purpose of this book is to offer readers important topics on the modeling, simulation, and optimization of distillation processes. The book is divided into four main sections: the first section is introduction to the topic, the second presents work related to distillation process modeling, the third deals with the modeling of phase equilibrium, one of the most important steps of distillation process modeling, and the the fourth looks at the reactive distillation process, a process that has been applied successfully to a number of applications and has been revealed as a promising strategy for a number of recent challenges
Steady-state detection, data reconciliation and machine learning for hybrid process modelling
In the process industry, it is possible to encounter systems whose behavior cannot be mapped through a first principles (white-box) model. Hybrid models aim at integrating data- driven (black-box) elements within white-box process models in order to fill the gap between the white-model model predictions and the actual system response. The goal of this Thesis is to propose and implement a hybrid modelling framework, and to assess its performance with respect to a white-box model
A Data-Driven Reaction Network for the Fluid Catalytic Cracking of Waste Feeds
Establishing a reaction network is of uttermost importance in complex catalytic processes such as fluid catalytic cracking (FCC). This step is the seed for a faithful reactor modeling and the subsequent catalyst re-design, process optimization or prediction. In this work, a dataset of 104 uncorrelated experiments, with 64 variables, was obtained in an FCC simulator using six types of feedstock (vacuum gasoil, polyethylene pyrolysis waxes, scrap tire pyrolysis oil, dissolved polyethylene and blends of the previous), 36 possible sets of conditions (varying contact time, temperature and catalyst/oil ratio) and three industrial catalysts. Principal component analysis (PCA) was applied over the dataset, showing that the main components are associated with feed composition (27.41% variance), operational conditions (19.09%) and catalyst properties (12.72%). The variables of each component were correlated with the indexes and yields of the products: conversion, octane number, aromatics, olefins (propylene) or coke, among others. Then, a data-driven reaction network was proposed for the cracking of waste feeds based on the previously obtained correlations.This research was funded by the Ministry of Economy and Competitiveness (MINECO) of the Spanish Government (CTQ2015-67425R and CTQ2016-79646-P), the European Regional Development Funds (ERDF) and the Basque Government (IT748-13). Hita is grateful for her postdoctoral grant awarded by the Department of Education, University and Research of the Basque Government (POS_2015_1_0035). Rodriguez is thankful to the University of the Basque Country UPV/EHU (Zabalduz Programme)
Multivariable System Identification of a Continuous Binary Distillation Column
Distillation is a process that is commonly used in industries for separation purpose. A
distillation column is a multivariable system which shows nonlinear dynamic
behavior due to its nonlinear vapor-liquid equilibrium. In order to gain better product
quality and lower energy consumption of the distillation column, an effective model
based control system is needed to allow the process to be operated over a certain
operating range. In control engineering, System Identification is considered as a well
suited approach for developing an approximate model for the nonlinear system. In this
study, System Identification technique is applied to predict the top and bottom
product composition by focusing the temperature of the distillation column. The
process in the column is based on the distillation of a binary mixture of Isopropyl
Alcohol and Acetone. The experimental data obtained from the distillation column
was used for estimation and validation of simulated models. During analysis, different
types of linear and nonlinear models were developed and are compared to predict the
best model which can be effectively used for designing the control system of the
distillation column. Among the linear models such as; Autoregressive with
Exogenous Input (ARX), Autoregressive Moving Average with Exogenous inputs
(ARMAX), Linear State Space (LSS) model and Continuous Process Model were
developed and compared with each other. The results of this comparison reveals that
the perf01mance of LSS model is efficient and hence it was further used to improve
the modeling approach and compared with other nonlinear models. A Nonlinear State
Space (NSS) model was developed by the combination of LSS and Neural Network
(NN) and is compared solely with NN and ANFIS identification model. The
simulation results show that the developed NSS model is well capable of defining the
dynan1ics of the plant based on the best fit criteria and residual performance. In
addition to this, NSS model predicted the best statistical measurement of the nonlinear
system. This approach is helpful for designing the efficient control system for online
separation process of the plant
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