Waste reduction is gaining importance as the preferred means of pollution prevention. Reactor network synthesis is one of the key parts of chemical process synthesis. In this study, a geometric approach to reactor net...Waste reduction is gaining importance as the preferred means of pollution prevention. Reactor network synthesis is one of the key parts of chemical process synthesis. In this study, a geometric approach to reactor network synthesis for waste reduction is presented. The bases of the approach are potential environment impact （PEI） rate-law expression, PEI balance and the instantaneous value of environmental indexes. The instantaneous value can be derived using the PEI balance, PEI rate-law expression and the environmental indexes. The optimal reactor networks with the minimum generation of potential environment impact are geometrically derived by comparing with areas of the corresponding regions. From the case study involving complex reactions, the approach does not involve solving the complicated mathematical problem and can avoid the dimension limitation in the attainable region approach.展开更多
It is believed that whether the instantaneous objective function curves of plug-flow-reactor (PFR) and continuous-stirred-tank-reactor (CSTR) overlap or not, they have a consistent changing trend for complex reactions...It is believed that whether the instantaneous objective function curves of plug-flow-reactor (PFR) and continuous-stirred-tank-reactor (CSTR) overlap or not, they have a consistent changing trend for complex reactions (steady state, isothermal and constant volume). As a result of the relation of the objective functions (selectivity or yield) to the instantaneous objective functions (instantaneous selectivity or instantaneous reaction rate), the optimal reactor network configuration can be determined according to the changing trend of the instantaneous objective function curves. Further, a recent partition strategy for the reactor network synthesis based on the instantaneous objective function characteristic curves is proposed by extending the attainable region partition strategy from the concentration space to the instantaneous objective function-unreacted fraction of key reactant space. In this paper, the instantaneous objective function is closed to be the instantaneous selectivity and several samples are examined to illustrate the proposed method. The comparison with the previous work indicates it is a very convenient and practical systematic tool of the reactor network synthesis and seems also promising for overcoming the dimension limit of the attainable region partition strategy in the concentration space.展开更多
This study presents the use of a new chemical reactor network(CRN) model and non-uniform injectors to predict the NOx emission pollutant in gas turbine combustor. The CRN uses information from Computational Fluid Dyna...This study presents the use of a new chemical reactor network(CRN) model and non-uniform injectors to predict the NOx emission pollutant in gas turbine combustor. The CRN uses information from Computational Fluid Dynamics(CFD) combustion analysis with two injectors of CH4-air mixture. The injectors of CH4-air mixture have different lean equivalence ratio, and they control fuel flow to stabilize combustion and adjust combustor's equivalence ratio. Non-uniform injector is applied to improve the burning process of the turbine combustor. The results of the new CRN for NOx prediction in the gas turbine combustor show very good agreement with the experimental data from Korea Electric Power Research Institute.展开更多
The global fuel management problem in BWRs(Boiling Water Reactors) can be understood as a very complex optimization problem,where the variables represent design decisions and the quality assessment of each solution is...The global fuel management problem in BWRs(Boiling Water Reactors) can be understood as a very complex optimization problem,where the variables represent design decisions and the quality assessment of each solution is done through a complex and computational expensive simulation.This last aspect is the major impediment to perform an extensive exploration of the design space,mainly due to the time lost evaluating non promising solutions.In this work,we show how we can train a Multi-Layer Perceptron(MLP) to predict the reactor behavior for a given configuration.The trained MLP is able to evaluate the configurations immediately,thus allowing performing an exhaustive evaluation of the possible configurations derived from a stock of fuel lattices,fuel reload patterns and control rods patterns.For our particular problem,the number of configurations is approximately 7.7×10^(10);the evaluation with the core simulator would need above 200 years,while only 100hours were required with our approach to discern between bad and good configurations.The later were then evaluated by the simulator and we confirm the MLP usefulness.The good core configurations reached the energy requirements,satisfied the safety parameter constrains and they could reduce uranium enrichment costs.展开更多
Wastewater treatment is a complicated dynamic process affected by microbial, chemical and physical factors. Faults are inevitable during the operation of modified sequencing batch reactors(MSBRs) because of the uncert...Wastewater treatment is a complicated dynamic process affected by microbial, chemical and physical factors. Faults are inevitable during the operation of modified sequencing batch reactors(MSBRs) because of the uncertainty of various factors. Abnormal MSBR results require fault diagnosis to determine the cause of failure and implement appropriate measures to adjust system operations. Bayesian network(BN) is a powerful knowledge representation tool that deals explicitly with uncertainty. A BN-based approach to diagnosing wastewater treatment systems based on MSBR is developed in this study. The network is constructed using the knowledge derived from literature and elicited from experts, and it is parametrized using independent data from a pilot test.A one-year pilot study is conducted to verify the diagnostic analysis. The proposed model is reasonable, and the diagnosis results are accurate. This approach can be applied with minimal modifications to other types of wastewater treatment plants.展开更多
An on-line prediction scheme combining the Karhunen-Love expansion and a recurrent neural network for a wall-cooled fixed-bed reactor is presented.Benzene oxidation in a pilotscale,single tube fixed-bed reactor is cho...An on-line prediction scheme combining the Karhunen-Love expansion and a recurrent neural network for a wall-cooled fixed-bed reactor is presented.Benzene oxidation in a pilotscale,single tube fixed-bed reactor is chosen as a working system and a pseudo-homogeneous twodimensional model is used to generate simulation data to investigate the prediction scheme presentedunder randomly changing operating conditions.The scheme consisting of the K-L expansion andneural network performs satisfactorily for on-line prediction of reaction yield and bed temperatures.展开更多
基金the Support Program for the Young Backbones of the College Teachers in Henan Province (No.[2005]461)the Key Technologies R &D Program of Henan Province (No.072102360052)
文摘Waste reduction is gaining importance as the preferred means of pollution prevention. Reactor network synthesis is one of the key parts of chemical process synthesis. In this study, a geometric approach to reactor network synthesis for waste reduction is presented. The bases of the approach are potential environment impact （PEI） rate-law expression, PEI balance and the instantaneous value of environmental indexes. The instantaneous value can be derived using the PEI balance, PEI rate-law expression and the environmental indexes. The optimal reactor networks with the minimum generation of potential environment impact are geometrically derived by comparing with areas of the corresponding regions. From the case study involving complex reactions, the approach does not involve solving the complicated mathematical problem and can avoid the dimension limitation in the attainable region approach.
基金Supported by the National Natural Science Foundation of China (No. 29776028, No. 29836140).
文摘It is believed that whether the instantaneous objective function curves of plug-flow-reactor (PFR) and continuous-stirred-tank-reactor (CSTR) overlap or not, they have a consistent changing trend for complex reactions (steady state, isothermal and constant volume). As a result of the relation of the objective functions (selectivity or yield) to the instantaneous objective functions (instantaneous selectivity or instantaneous reaction rate), the optimal reactor network configuration can be determined according to the changing trend of the instantaneous objective function curves. Further, a recent partition strategy for the reactor network synthesis based on the instantaneous objective function characteristic curves is proposed by extending the attainable region partition strategy from the concentration space to the instantaneous objective function-unreacted fraction of key reactant space. In this paper, the instantaneous objective function is closed to be the instantaneous selectivity and several samples are examined to illustrate the proposed method. The comparison with the previous work indicates it is a very convenient and practical systematic tool of the reactor network synthesis and seems also promising for overcoming the dimension limit of the attainable region partition strategy in the concentration space.
基金supported by Research Program supported by Konkuk University, Korea, 2010
文摘This study presents the use of a new chemical reactor network(CRN) model and non-uniform injectors to predict the NOx emission pollutant in gas turbine combustor. The CRN uses information from Computational Fluid Dynamics(CFD) combustion analysis with two injectors of CH4-air mixture. The injectors of CH4-air mixture have different lean equivalence ratio, and they control fuel flow to stabilize combustion and adjust combustor's equivalence ratio. Non-uniform injector is applied to improve the burning process of the turbine combustor. The results of the new CRN for NOx prediction in the gas turbine combustor show very good agreement with the experimental data from Korea Electric Power Research Institute.
基金Supported in part by Campus CEI-BioTic GENIL,from University of Granadasupport from Projects TIN2011-27696C02-01 from the Spanish Ministry of Economy and Competitiveness and P11-TIC-8001 from Andalusian Government+2 种基金the Departamento de Gestion de Combustible of the Comision Federal de Electricidad of Mexicothe support given by CONACyT from Mexico,through the research project CB-2011-01-168722the ININ through the research project CA-215
文摘The global fuel management problem in BWRs(Boiling Water Reactors) can be understood as a very complex optimization problem,where the variables represent design decisions and the quality assessment of each solution is done through a complex and computational expensive simulation.This last aspect is the major impediment to perform an extensive exploration of the design space,mainly due to the time lost evaluating non promising solutions.In this work,we show how we can train a Multi-Layer Perceptron(MLP) to predict the reactor behavior for a given configuration.The trained MLP is able to evaluate the configurations immediately,thus allowing performing an exhaustive evaluation of the possible configurations derived from a stock of fuel lattices,fuel reload patterns and control rods patterns.For our particular problem,the number of configurations is approximately 7.7×10^(10);the evaluation with the core simulator would need above 200 years,while only 100hours were required with our approach to discern between bad and good configurations.The later were then evaluated by the simulator and we confirm the MLP usefulness.The good core configurations reached the energy requirements,satisfied the safety parameter constrains and they could reduce uranium enrichment costs.
基金the Foundation of State Key Laboratory of Ocean Engineering of Shanghai Jiao Tong University(No.GKZD010071)
文摘Wastewater treatment is a complicated dynamic process affected by microbial, chemical and physical factors. Faults are inevitable during the operation of modified sequencing batch reactors(MSBRs) because of the uncertainty of various factors. Abnormal MSBR results require fault diagnosis to determine the cause of failure and implement appropriate measures to adjust system operations. Bayesian network(BN) is a powerful knowledge representation tool that deals explicitly with uncertainty. A BN-based approach to diagnosing wastewater treatment systems based on MSBR is developed in this study. The network is constructed using the knowledge derived from literature and elicited from experts, and it is parametrized using independent data from a pilot test.A one-year pilot study is conducted to verify the diagnostic analysis. The proposed model is reasonable, and the diagnosis results are accurate. This approach can be applied with minimal modifications to other types of wastewater treatment plants.
基金Supported by the National Natural Science Foundation of China(No.29676014)and others.
文摘An on-line prediction scheme combining the Karhunen-Love expansion and a recurrent neural network for a wall-cooled fixed-bed reactor is presented.Benzene oxidation in a pilotscale,single tube fixed-bed reactor is chosen as a working system and a pseudo-homogeneous twodimensional model is used to generate simulation data to investigate the prediction scheme presentedunder randomly changing operating conditions.The scheme consisting of the K-L expansion andneural network performs satisfactorily for on-line prediction of reaction yield and bed temperatures.
