生物质发电气化过程建模及优化研究
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摘要
开发“绿色能源”已成为当今世界上工业化国家开源节流、化害为利和保护环境的重要手段。以煤为主的能源结构是造成环境污染严重的主要原因,化石燃料的不可再生性和使用带来的环境污染,使生物质能在可再生能源结构中占据了重要地位。生物质气化发电技术是生物质能利用的一种重要形式,其中生物质气化过程又是气化发电技术的关键,虽然目前国内外已有一些这方面的研究报道,但是针对有关生物质气化过程特性进行建模及优化的研究则非常缺乏。因此,对于生物质气化发电系统中气化过程的建模和优化研究,具有重要的理论意义和实用价值,以及环境、社会效益和经济效益。
本文结合我国能源经济发展现状及生物质能特点,在对生物质发电热解气化过程机理分析基础上,对生物质气化过程建模和优化、焦油脱除过程建模和优化,以及生物质压缩成型过程建模和优化等作了做了系统的研究探讨。分别建立了气化过程的热化学平衡模型、最小二乘支持向量机模型、人工神经网络模型和循环流化床气化动力学模型,并依据实验数据对所建立的各模型做了仿真验证。结果表明,本文所建立的四种气化过程数学模型对实际生物质气化过程具有较好的模拟效果,能够有效地反映实际气化过程的特性。
基于建立的模型,提出了一种气化过程的多目标优化目标函数,主要目标是解决当气化过程的主要性能指标(如燃气有效组分含量、燃气热值、气化效率、产气率等)达到最大值时,气化过程可调整参数应当满足的条件,即控制参数的优化目标值。通过寻优计算,获得了当气化指标达到最大值时控制参数的优化目标值,验证了该目标函数的合理性和有效性。
以生物质稻秆为对象,采用石灰石作为催化剂对稻秆热解焦油进行催化裂解,由最小二乘支持向量机模型建立了生物质稻秆气化焦油催化裂解脱除过程的模型,并用遗传算法对模型参数进行了优化,得到了最佳的催化裂解温度和气相停留时间,使得焦油催化裂解率达到最高;以生物质木屑作为分析对象,对其进行热裂解脱焦,并依据最小二乘曲线拟合方法建立了生物质木屑气化焦油热裂解脱除过程的模型,用遗传算法对模型参数进行了优化,得到了最佳的热裂解温度和当量比,使得焦油含量达到最小。
基于最小二乘支持向量机建立了生物质锯末压缩成型过程的模型,并拟合了成型过程优化目标函数,依据实验数据对模型和优化目标函数进行了验证。结果表明,该模型能够较好的模拟成型过程特性,通过寻优计算,得到了当锯末压缩成型过程主要指标为最大值时可调参数的优化值。
本文的主要创新点有:
1.建立了四种生物质气化过程模型,即热化学平衡模型、最小二乘支持向量机模型、人工神经网络模型、循环流化床气化动力学模型;
2.提出一种生物质气化过程多目标优化目标函数;
3.建立了生物质气化(催化裂解、热裂解)焦油脱除过程模型;
4.提出了生物质气化焦油脱除过程优化目标函数;
5.建立了一种生物质压缩成型过程模型;
6.提出了一种生物质压缩成型过程的优化目标函数。
The development of "green energy" has become an important means in the industrialized countries of the world of cuting expenditure ,poducting new energy,converting crap to utilization and protecting the environment. The main reason for causing serious environmental pollution is the coal-dominated energy structure.The fossil fuels are non-renewable and the use of them can bring about environmental pollution,so the biomass occupied an important position in the renewable energy structure.Biomass gasification & power generation is an important form of biomass energy application,and biomass gasification process is the key of the technology of gasificaton & power generation.Although there have been some reports in this area at home and aboard,it is still very lack of the studies on modeling the characters of biomass gasification process and optimizing the parameters.So it has important theoretical and practical value, as well as environmental, social and economic benefits to study the modeling and optimization of the parameters about gasification process in the biomass gasification power generation system.
Combined with the economic development of the energy situation and based on the analysis of the mechanism of the biomass power generation pyrolysis gasification process,we had made a systematical study to explore the modeling and optimization of the biomass gasification process, tar removal process,as well as the biomass solidification process.We also respectively establish a gasification process of the thermo-chemical balance mechanism model,the least squares support vector machine model,the artificial neural network model and dynamic model of circulating fluidized bed gasification,and do a simulation based on the experimental data on the models which we had already established.The results show that four of mathematical models of gasification process established in this paper has a good simulation effect, they also can effectively reflect the actual characteristics of the gasification process and verify the effectiveness and feasibility of the model.
Based on the model, A multi-objective optimization objective function of gasification process had been made.The main objective is to solve that when the main performances and indexes of the gasification process (such as the gas content of effective components, gas calorific value, the efficiency of gasification, gas production rate, etc.) achieve the best value, the conditons of adjustable parameters should be meet to the gasification process,that is the optimization target of control parameters. Through the verification based on the optimization calculation,we have achieved the optimization target values when the gasification indexes reach the optimum,and we validated the the legitimacy and effectiveness of the objective function.
Targeting the biomass rice stalk,we use linestone as catalyst and cracking catalyse the tar which was pyrolysised from the target.We build the rice straw biomass gasification tar removal catalytic cracking process model by the least squares support vector machine model and optimize the model parameters through genetic algorithm.We also has gotten the best catalytic cracking temperature and gas residence time, making the highest rate of catalytic cracking tar.We made wood biomass as a target analysis so that we can thermally crack tar,and we can use genetic algorithms to optimize the model parameters that is based on the wood biomass gasification pyrolysis tar removal process model through the least-squares fitting method,then we got the best pyrolysis temperature and equivalence ratio,so that it is possible to make the tar content reach the minianum.
Based on the least squares support vector machine model,we had established the model of the biomass sawdust Compression molding process and fit the Optimization Parameters objective function of the molding process . On the basis of the experimental data ,we verified the model and the Optimization objective function, the results showed that the model can better simulated the molding process properties. After the Optimization Calculation,we obtained the max target value of the adjustable control Parameters when the main Indicators of the sawdust molding process reached the best value.
The main Innovation points of the article is as following:
1.Four biomass gasification process model had been established,such as the thermo-chemical balance mechanism model,the least squares support vector machine model,the artificial neural network model and the dynamic model of circulating fluidized bed gasification.
2.A multi-objective optimization objective function of the biomass gasification process had been made.
3.The tar removal model of the biomass gasification process by Catalytic Cracking or Pyrolysis Cracking had been established.
4.The tar removal Optimization objective function of the biomass gasification process had been made.
5.The model of the biomass Compression molding process had been established.
6.The Optimization objective function of the biomass Compression molding process had been made.
本文结合我国能源经济发展现状及生物质能特点,在对生物质发电热解气化过程机理分析基础上,对生物质气化过程建模和优化、焦油脱除过程建模和优化,以及生物质压缩成型过程建模和优化等作了做了系统的研究探讨。分别建立了气化过程的热化学平衡模型、最小二乘支持向量机模型、人工神经网络模型和循环流化床气化动力学模型,并依据实验数据对所建立的各模型做了仿真验证。结果表明,本文所建立的四种气化过程数学模型对实际生物质气化过程具有较好的模拟效果,能够有效地反映实际气化过程的特性。
基于建立的模型,提出了一种气化过程的多目标优化目标函数,主要目标是解决当气化过程的主要性能指标(如燃气有效组分含量、燃气热值、气化效率、产气率等)达到最大值时,气化过程可调整参数应当满足的条件,即控制参数的优化目标值。通过寻优计算,获得了当气化指标达到最大值时控制参数的优化目标值,验证了该目标函数的合理性和有效性。
以生物质稻秆为对象,采用石灰石作为催化剂对稻秆热解焦油进行催化裂解,由最小二乘支持向量机模型建立了生物质稻秆气化焦油催化裂解脱除过程的模型,并用遗传算法对模型参数进行了优化,得到了最佳的催化裂解温度和气相停留时间,使得焦油催化裂解率达到最高;以生物质木屑作为分析对象,对其进行热裂解脱焦,并依据最小二乘曲线拟合方法建立了生物质木屑气化焦油热裂解脱除过程的模型,用遗传算法对模型参数进行了优化,得到了最佳的热裂解温度和当量比,使得焦油含量达到最小。
基于最小二乘支持向量机建立了生物质锯末压缩成型过程的模型,并拟合了成型过程优化目标函数,依据实验数据对模型和优化目标函数进行了验证。结果表明,该模型能够较好的模拟成型过程特性,通过寻优计算,得到了当锯末压缩成型过程主要指标为最大值时可调参数的优化值。
本文的主要创新点有:
1.建立了四种生物质气化过程模型,即热化学平衡模型、最小二乘支持向量机模型、人工神经网络模型、循环流化床气化动力学模型;
2.提出一种生物质气化过程多目标优化目标函数;
3.建立了生物质气化(催化裂解、热裂解)焦油脱除过程模型;
4.提出了生物质气化焦油脱除过程优化目标函数;
5.建立了一种生物质压缩成型过程模型;
6.提出了一种生物质压缩成型过程的优化目标函数。
The development of "green energy" has become an important means in the industrialized countries of the world of cuting expenditure ,poducting new energy,converting crap to utilization and protecting the environment. The main reason for causing serious environmental pollution is the coal-dominated energy structure.The fossil fuels are non-renewable and the use of them can bring about environmental pollution,so the biomass occupied an important position in the renewable energy structure.Biomass gasification & power generation is an important form of biomass energy application,and biomass gasification process is the key of the technology of gasificaton & power generation.Although there have been some reports in this area at home and aboard,it is still very lack of the studies on modeling the characters of biomass gasification process and optimizing the parameters.So it has important theoretical and practical value, as well as environmental, social and economic benefits to study the modeling and optimization of the parameters about gasification process in the biomass gasification power generation system.
Combined with the economic development of the energy situation and based on the analysis of the mechanism of the biomass power generation pyrolysis gasification process,we had made a systematical study to explore the modeling and optimization of the biomass gasification process, tar removal process,as well as the biomass solidification process.We also respectively establish a gasification process of the thermo-chemical balance mechanism model,the least squares support vector machine model,the artificial neural network model and dynamic model of circulating fluidized bed gasification,and do a simulation based on the experimental data on the models which we had already established.The results show that four of mathematical models of gasification process established in this paper has a good simulation effect, they also can effectively reflect the actual characteristics of the gasification process and verify the effectiveness and feasibility of the model.
Based on the model, A multi-objective optimization objective function of gasification process had been made.The main objective is to solve that when the main performances and indexes of the gasification process (such as the gas content of effective components, gas calorific value, the efficiency of gasification, gas production rate, etc.) achieve the best value, the conditons of adjustable parameters should be meet to the gasification process,that is the optimization target of control parameters. Through the verification based on the optimization calculation,we have achieved the optimization target values when the gasification indexes reach the optimum,and we validated the the legitimacy and effectiveness of the objective function.
Targeting the biomass rice stalk,we use linestone as catalyst and cracking catalyse the tar which was pyrolysised from the target.We build the rice straw biomass gasification tar removal catalytic cracking process model by the least squares support vector machine model and optimize the model parameters through genetic algorithm.We also has gotten the best catalytic cracking temperature and gas residence time, making the highest rate of catalytic cracking tar.We made wood biomass as a target analysis so that we can thermally crack tar,and we can use genetic algorithms to optimize the model parameters that is based on the wood biomass gasification pyrolysis tar removal process model through the least-squares fitting method,then we got the best pyrolysis temperature and equivalence ratio,so that it is possible to make the tar content reach the minianum.
Based on the least squares support vector machine model,we had established the model of the biomass sawdust Compression molding process and fit the Optimization Parameters objective function of the molding process . On the basis of the experimental data ,we verified the model and the Optimization objective function, the results showed that the model can better simulated the molding process properties. After the Optimization Calculation,we obtained the max target value of the adjustable control Parameters when the main Indicators of the sawdust molding process reached the best value.
The main Innovation points of the article is as following:
1.Four biomass gasification process model had been established,such as the thermo-chemical balance mechanism model,the least squares support vector machine model,the artificial neural network model and the dynamic model of circulating fluidized bed gasification.
2.A multi-objective optimization objective function of the biomass gasification process had been made.
3.The tar removal model of the biomass gasification process by Catalytic Cracking or Pyrolysis Cracking had been established.
4.The tar removal Optimization objective function of the biomass gasification process had been made.
5.The model of the biomass Compression molding process had been established.
6.The Optimization objective function of the biomass Compression molding process had been made.
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