2,140 research outputs found

    Fast model predictive control for hydrogen outflow regulation in ethanol steam reformers

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    © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In the recent years, the presence of alternative power sources, such as solar panels, wind farms, hydropumps and hydrogen-based devices, has significantly increased. The reasons of this trend are clear: contributing to a reduction of gas emissions and dependency on fossil fuels. Hydrogen-based devices are of particular interest due to their significant efficiency and reliability. Reforming technologies are among the most economic and efficient ways of producing hydrogen. In this paper we consider the regulation of hydrogen outflow in an ethanol steam reformer (ESR). In particular, a fast model predictive control approach based on a finite step response model of the process is proposed. Simulations performed using a more realistic non-linear model show the effectiveness of the proposed approach in driving the ESR to different operating conditions while fulfilling input and output constraints.Peer ReviewedPostprint (author's final draft

    Modelling and Optimization of Primary Steam Reformer System Case Study: the Primary Reformer PT Petrokimia Gresik Indonesia

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    Steam reforming of hydrocarbons has been in use as the principal process for the generation of hydrogen and synthesis gas needed in the Ammonia production in petrochemical industries. Optimal operation of existing steam reformers is crucial in view of the high energy consumption and large value addition involved in the process. The economic objective of the process is determined by the cost of gas (Methane), the cost of steam and additional fuel. An optimum steam to gas ratio is expected from an optimum process control.  This can be applied on ratio control parameter for natural gas feed. In addition, steam Carbon Ratio is used to decrease coke formation in catalyst reformer. This paper presents the model identification and optimization of reforming control system of an industrial Primary Reformer at PT. Petrokimia Gresik (One of the fertilizer petrochemical industry in Indonesia). The reformer model has been approximated in the form of Takagi-Sugeno-Kang fuzzy inference system, with architecture in neural-network model. ANFIS (Adaptive Neuro-Fuzzy Inference System) has been utilized to determine NARX or ARX parameters model describing the dynamic of Industrial operational data have been used for training and validating the model. The optimization problem has been addressed through the utilization of Constrained Nonlinear Programming. The aim is to find the optimal process and ratio controller parameters to achieve the maximum Hydrogen formation. For a maximum fixed production rate of hydrogen produced by the unit, minimization of methane feed rate is chosen as the objective function to meet processing requirements.Keywords: Hydrocarbons, Model identification, ANFI

    Online Model Predictive Control of a Nonisothermal and Nonisobaric Membrane Reactor for Water-Gas Shift Reaction Applications

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    Online Model Predictive Control of a Nonisothermal and Nonisobaric Membrane Reactor for Water-Gas Shift Reaction Applications Jacob M. Douglas Modern hydrogen production units are tasked with producing the most hydrogen possible while dealing with flow variations caused by changing power demands. Classical methods for hydrogen production employing the water-gas shift reaction are governed by equilibrium limitations that take effect at high temperatures and high concentrations of H2 (Georgis, et al., 2014). The implementation of a membrane reactor with temperature control enables the hydrogen concentration and temperature to reach an equilibrium at a higher concentration of H2. Another challenge that is prevalent in this process is the cyclical hydrogen demand from changing downstream reforming process conditions. These challenges can be addressed by the implementation of advanced controllers that can cope with dynamic changes associated with different conditions, such as temperature oscillations and mitigation of hot spots. In this thesis, linear and nonlinear model predictive control (MPC) methods are implemented on a designed water-gas shift membrane reactor model in Aspen Custom Modeler. The implementation aim is to increase the production of hydrogen by considering the temperature control performed by manipulating the flow rates of the coolant entering the cooling jacket at different reactor zones as well as the reactor sweep flowrate. The control strategies considered for this application are: Quadratic iii Dynamic Matrix Control (QDMC), Nonlinear MPC (NMPC), and a Biomimetic-based controller cast as MPC (BIO-CS as MPC) (Mirlekar, et al., 2018) . The coolant usage is constrained by the use of quadratic programing (QP), sequential quadratic programing (SQP), or dynamic operations toolbox (DYNOP) solvers, depending on the employed MPC type, to match industrial standards. To mimic industrial conditions, the flowrate of hydrogen in the sweep stream is changed by +15% from its operating steady state. The MPC results that will be discussed show a successful increase in the production 1. Georgis, D., and Lima, F. V. (2014). Thermal Management of a Water-Gas Shift Membrane Reactor for High-Purity Hydrogen Production and Carbon Capture. Industrial & Engineering Chemistry Research, 7461-7469. 2. Mirlekar, G. V., Li, S., and Lima, F. V. (2017). Design and Implementation of a Biologically-inspired Optimal Control Strategy (BIO-CS) for Chemica

    Modelling and controlling of polymer electrolyte fuel cell systems

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    This thesis focuses on the modelling and controlling of polymer electrolyte fuelcell (PEFC) systems. A system level dynamic PEFC model has been developedto test the system performance (output voltage, reactants gas partial pressures,and stack temperature) for different operating conditions. The simulation resultsare in good agreement with the experimental data, which indicates that thePEFC model is well qualified to capture the dynamic performance of the PEFCsystem. Controlling strategies play a significant role in improving the fuel cellsystem’s reliability. Novel model predictive control (MPC) controllers and proportional–integral–derivative (PID) controllers are proposed and implemented indifferent PEFC systems to control voltage and regulate temperature to enhancesystem performance. MPC controllers show superior performance to PID controllers in tracking the reference value, with less overshoot and faster response. Anovel hydrogen selective membrane reactor (MR) is designed for methanol steamreforming (MSR) to produce fuel cell grade hydrogen for PEFC stack use. Thebackpropagation (BP) neural network algorithm is applied to find the mappingrelation between the MR’s operating parameters and the PEFC system’s outputperformance. Simulation results show that the BP neural network algorithm canwell predict the system behaviour and that the developed mapping relation modelcan be used for practical operation guidance and future control applications

    Control systems of offshore hydrogen production by renewable energies

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    Esta tesis trata sobre un proyecto de diseño de un Sistema de Gestión de Energía (SGE) que utiliza Modelo de Control Predictivo (MPC) para equilibrar el consumo de energía renovable con electrolizadores productores de hidrógeno. La energía generada se equilibra regulando el punto de operación y las conexiones de los electrolizadores usando un MPC basado en un algoritmo de Programación Mixta-Entera Cuadrática. Este algoritmo MPC permite tener en cuenta previsiones de energía, mejorando así el equilibrio y reduciendo el número de encendidos de los equipos. Se han realizado diferentes casos de estudio en instalaciones compuestas por unidades de generación de energía eléctrica a partir de energía renovable. Se considera la técnica de ósmosis inversa como paso intermedio para la producción de agua que alimenta a los electrolizadores. La validación se realiza utilizando datos meteorológicos medidos en un lugar propuesto para el sistema, mostrando el funcionamiento adecuado del SGE desarrollado.Departamento de Ingeniería de Sistemas y AutomáticaDoctorado en Ingeniería Industria

    Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks

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    The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory.Ministerio de Ciencia, Innovación y Universidades PGC2018-095322-B-C22Comunidad de Madrid P2018/TCS-4566Unión Europea P2018/TCS-456

    Solid oxide fuel cell hybrid system: A detailed review of an environmentally clean and efficient source of energy

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    This paper reports a review of an environmentally clean and efficient source of energy such as solid oxide fuel cell hybrid systems. Due to climate concerns, most nations are seeking alternative means of generating energy from a clean, efficient and environmental-friendly method. However, this has proven a big hurdle for both academic and industry researchers over many years. Currently, practical and technically feasible solution can be obtained via an integration of a microturbine and a fuel cell (hybrid systems). Combining the two distinct systems in a hybrid arrangement the efficiency of the microturbine increases from 25 to 30% to the 60-65% range. Hence, this paper outlines an engineering power generation solution towards the acute global population growth, the growing need, environmental concerns, intelligent use of energy with attendant environmental and hybrid system layouts concerning arising problems and tentative proposed solutions. Furthermore, advantages of a solid oxide fuel cell hybrid systems with respect to the other technologies are identified and discussed rationally. Special attention is devoted to modelling with software and emulator rigs and system prototypes. The paper also reviews the limitations and the benefits of these hybrid systems in relationship with energy, environment and sustainable development. Few potential applications, as long-term potential actions for sustainable development, and the future of such devices are further discussed

    Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

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    An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes
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