The future of bioethanol

Yeasts have been domesticated by mankind before horses. After the mastering of alcoholic fermentation for centuries, yeasts have become the protagonist of one of the most important biotechnological industries worldwide: the production of bioethanol. This chapter will initially present some important challenges to be overcome in this industry, both in first and second generation biofuel production. Then, it will briefly revisit some advances obtained in recent years. Finally, it will present and discuss some opportunities, in the scope of metabolic engineering and synthetic biology, that will likely be present in the future of bioethanol

125th anniversary review: fuel alcohol: current production and future challenges

Global research and industrial development of liquid transportation biofuels are moving at a rapid pace. This is mainly due to the significant roles played by biofuels in decarbonising our future energy needs, since they act to mitigate the deleterious impacts of greenhouse gas emissions to the atmosphere that are contributors of climate change. Governmental obligations and international directives that mandate the blending of biofuels in petrol and diesel are also acting as great stimuli to this expanding industrial sector. Currently, the predominant liquid biofuel is bioethanol (fuel alcohol) and its worldwide production is dominated by maize-based and sugar cane-based processes in North and South America, respectively. In Europe, fuel alcohol production employs primarily wheat and sugar beet. Potable distilled spirit production and fuel alcohol processes share many similarities in terms of starch bioconversion, fermentation, distillation and co-product utilisation, but there are some key differences. For example, in certain bioethanol fermentations, it is now possible to yield consistently high ethanol concentrations of ~20% (v/v). Emerging fuel alcohol processes exploit lignocellulosic feedstocks and scientific and technological constraints involved in depolymerising these materials and efficiently fermenting the hydrolysate sugars are being overcome. These so-called secondgeneration fuel alcohol processes are much more environmentally and ethically acceptable compared with exploitation of starch and sugar resources, especially when considering utilisation of residual agricultural biomass and biowastes. This review covers both first and second-generation bioethanol processes with a focus on current challenges and future opportunities of lignocellulose-to-ethanol as this technology moves from demonstration pilot-plants to full-scale industrial facilities

Lignocellulosic Ethanol: Technology and Economics

The accelerated global warming calls for fast development of solutions to curb excessive Greenhouse gas emission. Like most of other forms of renewable energy, lignocellulosic ethanol can help the human beings mitigate the climate deterioration and gain independence from fossil fuels. This chapter gives a survey of bioethanol production in the U.S. and world, describes classifications of three generations of bioethanol, provides an overview of all the stages of currently adopted process for the second-generation bioethanol production, briefs on new development on enzymes for hydrolysis and fermentation and new processes for ethanol generation, summarizes on recent life-cycle assessments of greenhouse gas emission and techno-economic evaluation of ethanol production. To sustain the infant cellulosic ethanol industry, substantial improvement in the following areas need to happen in a timely manner: (1) Effective and low-cost biomass pretreatment method, (2) efficient fermentation of all sugars released during the pretreatment and hydrolysis steps, (3) development of enzymes that tolerate various inhibitors including monosaccharides (mainly glucose) and ethanol, and (4) heat-tolerant fermentation microbes and enzymes for efficient simultaneous saccharification and fermentation. Genetic engineering is expected to play a key role in addressing most of the issues in these areas

A Comprehensive Optimization Framework for Designing Sustainable Renewable Energy Production Systems

As the world has recognized the importance of diversifying its energy resource portfolio away from fossil resources and more towards renewable resources such as biomass, there arises a need for developing strategies which can design renewable sustainable value chains that can be scaled up efficiently and provide tangible net environmental benefits from energy utilization. The objective of this research is to develop and implement a novel decision-making framework for the optimal design of renewable energy systems. The proposed optimization framework is based on a distributed, systematic approach which is composed of different layers including systems-based strategic optimization, detailed mechanistic modeling and operational level optimization. In the strategic optimization the model is represented by equations which describe physical flows of materials across the system nodes and financial flows that result from the system design and material movements. Market uncertainty is also incorporated into the model through stochastic programming. The output of the model includes optimal design of production capacity of the plant for the planning horizon by maximizing the net present value (NPV). The second stage consists of three main steps including simulation of the process in the simulation software, identification of critical sources of uncertainties through global sensitivity analysis, and employing stochastic optimization methodologies to optimize the operating condition of the plant under uncertainty. To exemplify the efficacy of the proposed framework a hypothetical lignocellulosic biorefinery based on sugar conversion platform that converts biomass to value-added biofuels and biobased chemicals is utilized as a case study. Furthermore, alternative technology options and possible process integrations in each section of the plant are analysed by exploiting the advantages of process simulation and the novel hybrid optimization framework. In conjunction with the simulation and optimization studies, the proposed framework develops quantitative metrics to associate economic values with technical barriers. The outcome of this work is a new distributed decision support framework which is intended to help economic development agencies, as well as policy makers in the renewable energy enterprises