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 ii 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

Chapter Machine Learning Models for Industrial Applications

More and more industries are aspiring to achieve a successful production using the known artificial intelligence. Machine learning (ML) stands as a powerful tool for making very accurate predictions, concept classification, intelligent control, maintenance predictions, and even fault and anomaly detection in real time. The use of machine learning models in industry means an increase in efficiency: energy savings, human resources efficiency, increase in product quality, decrease in environmental pollution, and many other advantages. In this chapter, we will present two industrial applications of machine learning. In all cases we achieve interesting results that in practice can be translated as an increase in production efficiency. The solutions described cover areas such as prediction of production quality in an oil and gas refinery and predictive maintenance for micro gas turbines. The results of the experiments carried out show the viability of the solutions

elopment of Neural Network Model for Predicting Crucial Product Properties or Yield for Optimisation of Refinery Operation

Refinery optimisation requires accurate prediction of crucial product properties and yield of desired products. Neural network modeling is an alternative approach to prediction using mathematical correlations. The project is an extension of a previous research conducted by the university on product yield and properties prediction using non-linear regression method. The objectives of this project are to develop a framework for the application of neural network modeling in predicting refinery product yield and properties, to develop neural network model for three case studies (predicting crude distillation yield, diesel pour point and hydrocracker total gasoline yield) and to evaluate the suitability of using neural networkmodelingfor predicting refinery product yield and properties. The project methodologies used are literature research and computer modeling using MATLAB neural network toolbox. The framework development for neural network modeling include aspects such as process understanding, data collection and division, input elements selection, data preprocessing, network type selection, design of network architecture, learning algorithm selection, network training, and network simulation using new data set. Various configurations of neural network model were tested to choose the best model to represent each case study. The model selected has the smallestmean squared error when simulated using test data. The results are presented in the form of the network configuration that gives the smallest MSE, plots comparing the actual output with the output predictedby the neural network, as well as residual analysis results to determine the range of deviationbetween the actual and predicted output. Although the accuracy of the output predicted by the neural network model requires further improvement, in general, the study has shown the tremendous potential for the use of neural networkfor predicting refinery product yield and properties. Suggestions for future study in the area include improvement of the model accuracy using advanced methods such as cross-training and stacked network, integration of neural networkwith plant's Advanced Process Control as inferential property predictor, and study on inverted network for use in a neural network-based controller

Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications

Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit

Analysis and modelling of microalgae growth and production of high added-value metabolites

Microalgae are a very versatile microorganism that have the ability to modify their biomass composition under controlled condition in order to accumulate products having applications in several sectors. The aim of this thesis work is the analysis and modelling of both microalgal growth and production of high added-value metabolites, focusing also on their extraction and purification. An outdoor 10 bubble column photobioreactors (PBRs) pilot plant for the cultivation of two microalgae named Tetradesmus obliquus and Graesiella emersonii, covering a 9 months cultivation period (March 2017-December 2017), has been installed and operated. All collected data (as microalgal growth rate, outdoor parameters and initial cultivation’s conditions) have been used to develop an empirical model for prediction of microalgal growth in photobioreactors at specific outdoor conditions, using Principal Component Analysis and Partial Least Squares regression method, obtaining acceptable outcomes for both responses: microalgal specific growth rate (μ) and maximum productivity (Pmax). Concerning microalgal metabolism, also a new mathematical model able to represent in a simple way the accumulation of metabolites inside microalgae, focusing on the carbon partitioning process between triacylglycerides (TAG) and starch during nitrogen starvation in phototrophy, has been developed, obtaining high R-Squared values as index of model’s goodness of fitting. A future application of these models can be found in the MEWLIFE European project, in which Bio-P has a role as partner, since this project has as aim the production of microalgal biomass in an integrated phototrophic and heterotrophic cultivation system using preconcentrated olive oil wastewaters (OMWW) as carbon source. As a completion of the microalgal process treatment, a study of the downstream processes for the extraction (using supercritical CO2) and purification of the high added value metabolites (with molecular distillation) has been carried out, developing a feasibility study also from the economical point of view. As regard the supercritical CO2 the best extraction conditions in terms of operative variables have been: T = 60°C, P = 250 bar and SSR = 5 h−1 with a daily amount of the desired products equal to 147 kg and OPEX = 561.7 k€/year and CAPEX = 2717.9 k€/year. Regarding the molecultar distillation process, the best operating conditions have been found at T = 128 °C and P = 0.33 Pa, obtaining OPEX = 498.23 k€/year and CAPEX = 2387.4 k€/year