28 research outputs found

    Nonparametric nonlinear model predictive control

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    Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC

    Distributed control of chemical process networks

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    Novel Saturated Flow Boiling Heat Transfer Correlation for R32 Refrigerant

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    A comprehensive approach for optimizing a biomass assisted geothermal power plant with freshwater production: Techno-economic and environmental evaluation

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    In the current research, a comprehensive thermodynamic study of an innovative biomass-geothermal power plant combined with a desalination system is presented and analyzed. The suggested system is a geothermal based cascaded steam/organic Rankine cycle benefiting from municipal solid waste combustion in order to enhance its performance. Besides, the exhaust gasses of municipal solid waste combustion are utilized as the primary energy source for driving a multi-effect desalination subsystem which converts the seawater into low salinity water. A comprehensive approach including energy and exergy analyses along with thermoeconomic evaluation is applied to investigate the viability of the plant. First of all, validation of the presented model has been tested by means of comparing the results with published data, through which a good agreement has achieved. The energy and exergy efficiencies can be reached to 13.9% and 19.4% respectively while the total product cost rate of the system is estimated to be 285.3 /h.Moreover,environmentalanalysisisconductedintermsofestimationofCO2andNOxemissionstoaddresstheenvironmentalbenefitsofutilizingmunicipalsolidwastecombustioninsteadofcoalforimprovingtheperformanceofthegeothermalpowerplant.Resultsindicatethatmunicipalsolidwasteutilizationsaves8,092tonnesofCO2emissionand36tonnesofNOxemissionannuallyinrelativetocoalutilization.Finally,athreeobjectiveoptimizationisperformedregardingexergyefficiency,totalproductcostrate,andCO2emissionrateasobjectivefunctionsthroughapplyingtheGeneticAlgorithminordertofigureouttheoptimumperformanceofthesystemandtheParetofrontierisextracted.Theresultsofoptimizationindicatethatintheoptimumcase,exergyefficiencyof20.72/h. Moreover, environmental analysis is conducted in terms of estimation of CO2 and NOx emissions to address the environmental benefits of utilizing municipal solid waste combustion instead of coal for improving the performance of the geothermal power plant. Results indicate that municipal solid waste utilization saves 8,092 tonnes of CO2 emission and 36 tonnes of NOx emission annually in relative to coal utilization. Finally, a three-objective optimization is performed regarding exergy efficiency, total product cost rate, and CO2 emission rate as objective functions through applying the Genetic Algorithm in order to figure out the optimum performance of the system and the Pareto frontier is extracted. The results of optimization indicate that in the optimum case, exergy efficiency of 20.72%, total product cost rate of 306.1 /h and CO2 emission rate of 1967.7 tonnes/y are achievable. © 2020 Elsevier Lt

    Crystal structure, stability, and electronic properties of hydrated metal sulfates MSO4(H2O)(n) (M=Ni, Mg; n=6, 7) and their mixed phases : A first principles study

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    Removal of Mg from hydrated Ni sulfates has long been a problem in the industrial purification process of hydrated Ni sulfates. In this work, we have investigated this industrial problem using state-of-the-art molecular simulations. Periodic Density Functional Theory (DFT) and cluster DFT calculations are used to study the crystal structures and phase stability of the hexahydrated and heptahydrated Ni and Mg sulfates and their mixed phases. The calculated lattice parameters of MSO4(H2O)(n) (M=Ni, Mg; n=6, 7) crystals are in good agreement with available experimental data. The relative energy differences of the mixed phase for both hexahydrated and heptahydrated Ni/Mg sulfates obtained from both the periodic and cluster DFT calculations are generally less than kT (25.8 meV, T=300 K), indicating that a continuous solid solution is formed. We also investigated the Bader charges and electronic structures of the hexahydrated and heptahydrated Ni/Mg sulfates using the periodic DFT calculations. The energy band gaps of the hexahydrated and heptahydrated Ni and Mg sulfates were predicted by first-principles calculations. Large energy band gaps of about similar to 5.5 eV were obtained from the DFT-GGA calculations for hydrated Mg sulfates, and band gaps of about similar to 5.1 eV were obtained by the DFT-GGA+U calculations for hydrated Ni sulfates. (C) 2014 Elsevier Ltd. All rights reserved