Modelling of a post-combustion CO₂ capture process using neural networks

This paper presents a study of modelling post-combustion CO₂ capture process using bootstrap aggregated neural networks. The neural network models predict CO₂ capture rate and CO₂ capture level using the following variables as model inputs: inlet flue gas flow rate, CO₂ concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. In order to enhance model accuracy and reliability, multiple feedforward neural network models are developed from bootstrap re-sampling replications of the original training data and are combined. Bootstrap aggregated model can offer more accurate predictions than a single neural network, as well as provide model prediction confidence bounds. Simulated CO₂ capture process operation data from gPROMS simulation are used to build and verify neural network models. Both neural network static and dynamic models are developed and they offer accurate predictions on unseen validation data. The developed neural network models can then be used in the optimisation of the CO₂ capture process

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

Prediction of Vapour-Liquid Equilibrium Data Using Neural Network for Hydrocarbon Ternary System (ETHANE-PROPANE-N-BUTANE)

The prediction of vapour- liquid equilibrium is useful in process simulation and control as well making process engineering design decisions. Prediction of vapour-liquid equilibrium data was carried out using MATLAB software. Pre-existing data of hydrocarbon ternary system (ethane-propane-n-butane) in terms of phase composition, temperature and pressure was trained by iteratively adjusting networks, initializing weights and biases to minimize the network performance function net. MATLAB a software package containing artificial neural network was employed to predict the point where there is no change in composition of both liquid and vapour formed when liquid mixtures of ethane-propane-n-butane vapourises. Predicted values show reasonable and good correlation results when compared to the experimental data thus indicating that the network is an efficient and a good prediction tool for vapour-liquid equilibrium ternary systems

Development of surrogate models for distillation trains

El temps d’execució necessari per a la resolució de problemes d’optimització en programes de simulació rigorosos no sol ser asequible, fet que promou l’ús de models de substitució. El desenvolupament d’aquests models aproximats comporta la resolució d’una sèrie de reptes com la càrrega computacional i el risc d’excés d’adequació del model. En el treball presentat, les eines i procediments per a crear, entrenar i validar una xarxa neuronal (ANN) son desenvolupats per a l’entrenament de models de simplificació de simulacions rigoroses. Les eines proposades han estat posades a prova en un cas d’estudi que aborda la síntesis de trens de separació per als productes de la pirólisis del polietilé, centrant-se en les columnes de destil·lació del procés simulades en Aspen-HYSYS. Finalment, dos models ANN que simulen el comportament de la columna respecte una funció que considera els costos de la simulació han estat desenvolupats. El comportament i precisió dels dos models és correspon a l’estudiat en la superfície triada.El tiempo de computación necesario para solucionar problemas de optimización en programas de simulación rigurosos no suele ser asequible, lo que promueve el uso de modelos de sustitución. El desarrollo de estos modelos aproximados conlleva la resolución de una serie de retos como la carga computacional y el riesgo de sobreajuste del modelo. En el presente trabajo, las herramientas y procedimientos para crear, entrenar y validar una red neuronal artificial (ANN), han sido desarrollados para la construcción de modelos simplificados de simulaciones rigurosas. Las herramientas propuestas han sido puestas a prueba en un caso de estudio que aborda la síntesis de trenes de separación para los productos de la pirolisis del polietileno, centrándose en las columnas de destilación del proceso simuladas en Aspen-HYSYS. Finalmente, dos modelos de redes neuronales que simulan el comportamiento de la columna con respecto a una función que considera los costes de la simulación han sido desarrollados. Los dos modelos representan correctamente y con buena precisión la superficie estudiada.The computational time required to solve optimization problems in rigorous simulation programs is usually unaffordable, raising the need to use surrogate models. The development of these approximate models is a challenge that needs to handle the computational burden and risk of over fitting. In the present work, tools and procedures to build, train, and validate an Artificial Neural Network (ANN) are developed to build simplified models of rigorous simulations. The proposed tools are tested with a case study that addresses the synthesis of separation trains for the products of polyethylene pyrolysis, focusing in the distillation columns of the process simulated with Aspen-HYSYS. Finally, two ANN models have been developed to simulate the behaviour of the column regarding a function that considers the costs of the simulation. Both models fit correctly and show good accuracies with respect the surface studied

Performance assessment of an NH3/LINO3 bubble plate absorber applying a semi-empirical model and artificial neural networks

In this study, ammonia vapor absorption with NH3/LiNO3 was assessed using correlations derived from a semi-empirical model, and artificial neural networks (ANNs). The absorption process was studied in an H-type corrugated plate absorber working in bubble mode under the conditions of an absorption chiller machine driven by low-temperature heat sources. The semi-empirical model is based on discretized heat and mass balances, and heat and mass transfer correlations, proposed and developed from experimental data. The ANN model consists of five trained artificial neurons, six inputs (inlet flows and temperatures, solution pressure, and concentration), and three outputs (absorption mass flux, and solution heat and mass transfer coefficients). The semi-empirical model allows estimation of temperatures and concentration along the absorber, in addition to overall heat and mass transfer. Furthermore, the ANN design estimates overall heat and mass transfer without the need for internal details of the absorption phenomenon and thermophysical properties. Results show that the semi-empirical model predicts the absorption mass flux and heat flow with maximum errors of 15.8% and 12.5%, respectively. Maximum errors of the ANN model are 10.8% and 11.3% for the mass flux and thermal load, respectively

MODELLING OF CO2 SOLUBILITY IN DIETHANOLAMINE, NMETHYLDIETHANOLAMINE AND THEIR MIXTURES USING ARTIFICIAL NEURAL NETWORK

Natural gas has a wide range of acid gas concentrations, from parts per million to 50 volume percent and higher, depending on the nature of the rock formation from which it comes. Because of the corrosiveness of H2S and CO2 in the presence of water and because of the toxicity of H2S and the lack of heating value of CO2, sales gas is required to be sweetened to contain no more than a quarter grain H2S per 100 standard cubic feet (4 parts per million) and to have a heating value of no less than 920 to 980 Btu/SCF, depending on the contract. The most widely used processes to sweeten natural gas are those using the alkanolamines, and of the alkanolamines the two most common are n-methyldiethanolamine (MDEA) and diethanolamine (DEA). In this research, data from Khalid Osman et al (2012), A. Benamor et al (2005) and Zhang et al (2002) will be used to simulate the solubility of CO2 in MDEA + DEA aqueous solution using ANN model and the performance will be compared to show which model is better for CO2 absorption. Besides, the study of CO2 solubility in MDEA and DEA aqueous solution respectively will be using data from Jou et al (1982) and Lee et al (1972) works and simulation of ANN model was used to compare the performance between ANN model and the reference research works mentioned earlier. Developed model has an absolute relative deviation (δAAD) of 8.71% while δAAD for data from Khalid Osman et al (2012), A. Benamor et al (2005) and Zhang et al (2002) are 17.06%, 12.09% and 9.82% respectively. In terms of pure amine prediction, ANN model of CO2 solubility predicted in pure MDEA has δAAD of 8.29% while the reference paper which is A. Benamor et al (2005) has absolute relative deviation of 10.76%. For prediction in pure DEA, the model has δAAD of 3.33% compared to reference paper which is also from A. Benamor et al (2005) with 4.72%. ANN has great ability to predict CO2 solubility in pure MDEA, DEA, and their mixtures only by developing models for each situation and condition due to the limitation of ANN itself which cannot simulate the new input data if they do not have same patterns with the one that has been used to develop the model