Alkaline bioconversions for biofuel production from sunlight

生物质甲烷是化石燃料的潜在可再生替代品。通过用光营养微生物固定大气中的二氧化碳，可以产生用于生产甲烷的生物质。我们建议的基本创新是在高pH(9-10)下培养藻类/蓝藻，以有效地吸收二氧化碳，然后直接将产生的生物质转化为甲烷。众所周知，在高pH值的苏打湖中，光营养和生物量消化都是自然发生的。然而，这些过程在高pH值下的生物技术潜力尚未被探索。这项研究的目的是探索这一潜力，研究在高pH条件下利用阳光和大气生产甲烷的技术和经济可行性。使用高pH值有一些潜在的优势：(1)吸收二氧化碳的效率将大大提高；(2)高pH值下的生物质消化将产生不含二氧化碳的甲烷，可在国家天然气电网中直接用作绿色气体；(3)消化过程中产生的二氧化碳可反应成重碳酸盐，可直接循环到光养生物。研究的重点将是在高pH生物反应器中从高pH碱湖中有控制地选择藻类/蓝藻和产甲烷微生物群落。这些生物反应器将被用来深入了解所有相关的过程：二氧化碳吸收、光养生物的生态生理、生物质发酵为甲烷的生物瓶颈以及降水过程。这些见解将被浓缩在一个数学模型和流程设计规则中，从而使整个流程能够迅速扩大。

Methane from biomass is a potential renewable replacement of fossil fuels. Biomass for methane production can be generated by fixing CO2 from the atmosphere with phototrophic microorganisms. The basic novelty of our proposal is to grow algae/cyanobacteria at high pH (9-10) to absorb CO2 efficiently and then directly convert the produced biomass to methane. It is known that both phototrophy and biomass digestion occur naturally in high-pH soda lakes. However, the biotechnological potential of these processes at high pH has not yet been explored. The aim of the research is to explore this potential, to investigate the technical and economical feasibility of methane production from sunlight and the atmosphere at high pH. Use of high pH has a number of potential advantages: (1) CO2 absorption will be much more efficient; (2) biomass digestion at high pH will deliver CO2-free methane that can be used directly as green gas in the national gas grid; (3) the CO2 produced during digestion reacts to bicarbonate that can be recycled directly to the phototrophs. Focus of the research will be the controlled selection of algal/cyanobacterial and methanogenic microbial communities from high-pH soda lakes in high-pH bioreactors. These bioreactors will be used to acquire insight into all relevant processes: CO2 absorption, the ecophysiology of the phototrophs, the biological bottlenecks of the fermentation of biomass to methane, and precipitation processes. The insight will be condensed in a mathematical model and process design rules enabling the rapid scale up of the overall process.