Multi-objective Superstructure Optimization Of Biomass To Biomethane Conversion Process

Due to severe fluctuations in fossil energy prices and serious environmental issues, Biomethane, as a reproducible and environmentally friendly fuel that can decrease greenhouse gas emissions and reduce the non-renewable energy consumption, has gotten increasing attentions. Biomass to biomethane conversion system, which covers the whole subsystem including biomass collection and transportation, pretreatment anaerobic digestion, biogas upgrading, and digestate utilization, is a complex process. Efforts on the system collaborative integration and optimization of these processes are essential to maintain the critical balance among energy, economy and environment. In this paper, the environmental impacts of biomass and digestate are developed using the green degree(GD) method. A multi-criteria mixed integer non-linear programming(MINLP) model for the rigorous optimization of the superstructure-based biomethane production process is developed. The model simultaneously considers the minimization of the energy consumption, the minimization of the total environmental impact, and maximization of the biomethane production as three objective functions and considers temperature, pressure, and recovery ratio, etc. as the optimized variables, subject to mass balance constraints, energy balance constraints, technology selection constraints, and environmental impact constraints. Finally, the complicated non-linear optimization problem is solved with a fast nondominated sorting geneticalgorithm II(NSGA-II) to obtain a set of Pareto optimal solutions. The major work and innovative results of the dissertation are as follows.(1) Based on Aspen software and literature resources, energy consumption mathematical models of complex biomass to biomethane system and key techniques of the subsystem are established, which is used to probe into the relationship among the operation parameters, techniques and material-energy utilization. The results reveal that the main energy consuming sections are from anaerobic digestion and biogas upgrading. And the optimal material-energy utilization can be obtained via digestate waste heat utilization, thermophilic digestion and ionic liquid scrubbing(ILS).(2) Based on GD method, the GD values of biomass and digestate are developed in different ways of pollution. Meanwhile, the environmental impact of biomass and digestate is quantitatively assessed and analyzed. The results show that the GD values of biomass emission are more than that of biomass combustion. The GD values of agricultural residues resources combustion are more than that of manure combustion. The GD value of pig manure is the least. So the influence on environment is the worst. Meanwhile, the green degree model of the biomass to biomethane conversion process is established. And the factors influencing Environment proformance are explored. The results show that if ILS technique is selected, the GD value of the whole system is more than zero, which indicates that the whole biomethane production process is friendly to environment.(3) Based on mathematical programming method. The superstructure optimization model of biomass to biomethane system is conducted. The system encompasses biofeedstock collection and transportation, anaerobic digestion, biogas upgrading, and digestate recycling. The model seeks to minimize the specific energy consumption and maximize the specific green degree and the specific biomethane production by optimizing the combinations of the feedstocks, operation variables, and alternative operation technologies. It is applied to a case study for the integration and optimization of the biogas project in Nanjing University of Technology. The mathematical model is a mixed-integer nonlinear problem, which is solved with a fast and elitist genetic algorithm: NSGA-II to obtain a set of Pareto optimal solutions, and the Pareto optimal surface is shown in using linear interpolation of the Pareto non-inferior solutions. The resulting Pareto-optimal solution surface reveals quantitatively the trade-off among the three conflicting objectives. For ILS technique selected, the specific GD and biomethane production objectives can be optimized, which leads to higher energy consumption. For PWS technology selected, the specific biomethane production increases at the expense of deteriorating the energy consumption and environmental impact performance.