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

Tomada de decisão em fase inicial de projeto : análise do risco econômico em adicionar um processo novo a uma usina existente

Orientadores: Roger Josef Zemp, Valdir Apolinário de FreitasDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuímicaResumo: Em fases iniciais de projetos de plantas químicas, são estudados aspectos dos processos que são definidos, em muitos casos, de acordo com o conhecimento e as experiências passadas das pessoas envolvidas no projeto. Além disso, decisões são tomadas com base em modelos econômicos que descrevem apenas um momento no tempo e não passam informações sobre possíveis cenários alternativos. Este trabalho objetiva desenvolver e demonstrar uma metodologia que pode auxiliar times de projeto na tomada de decisões e planejamento de recursos através da análise de risco econômica de projeto utilizando simulações de Monte Carlo. Três exemplos foram construídos para exemplificar o método; o primeiro é uma avaliação da integração de uma planta de biobutanol a uma usina de cana de açúcar existente, constituindo uma biorrefinaria, o segundo é a análise de um processo de produção de ácido mucônico a partir de biomassa usando pouca quantidade de informação, e o terceiro é a avaliação de uma tecnologia de produção de açúcar lignocelulósico e seu potencial como fornecedora de matéria prima para a química renovável. Os resultados mostram que uma integração de biorrefinaria, um processo e mesmo uma proposta de tecnologia podem ser avaliados com sucesso através da análise de risco econômica, a probabilidade de se atingir um determinado resultado econômico pode ser calculada e os principais fatores que influenciam nos resultados podem ser claramente identificados. Conclui-se que a análise de risco através de simulações de Monte Carlo é uma ferramenta importante a ser usada em projetos de química renovávelAbstract: In the early stages of chemical process projects, aspects of the process are developed, which are defined, in many cases, by previous knowledge and past experiences of the people involved. Moreover, business decisions to carry on, stop, or modify the project plan are often taken based on economic models that describe a still photograph in time, and don't provide sufficient insights on possible scenarios. This work intends to develop and demonstrate a methodology that could help decision making and allocating of development resources through economic risk analysis of a project by using Monte Carlo simulations. Three examples were built in order to test and exemplify the method; the first is an assessment of the integration of a biobutanol plant as an add-on plant in an existing sugarcane mil constituting a biorefinery. The second, is the evaluation of a muconic acid from biomass production process using low level of information available, and, the third, is the analysis of a lignocellulosic sugar production technology and its prospect as raw material provider for renewable chemistry. The results show that full biorefinery integration, a process and even a single technology proposition can be successfully assessed through economic risk analysis, the probability of achieving the desired economic results can be calculated, and, the main factors influencing the results can be identified, allowing to conclude that the economic risk analysis through Monte Carlo simulations is an efficient tool to be used in renewable chemistry projectsMestradoSistemas de Processos Quimicos e InformaticaMestre em Engenharia Químic

Extensive Sensitivity Analysis and Parallel Stochastic Global Optimization Using Radial Basis Functions of Integrated Biorefineries under Operational Level Uncertainties

This work presents a decision-making framework for global optimization of detailed renewable energy processes considering technological uncertainty. The critical uncertain sources are identified with an efficient computational method for global sensitivity analysis, and are obtained in two different ways, simultaneously and independently per product pathway respect to the objective function. For global optimization, the parallel stochastic response surface method developed by Regis & Shoemaker (2009) is employed. This algorithm is based on the multi-start local metric stochastic response surface method explored by the same authors (2007a). The aforementioned algorithm uses as response surface model a radial basis function (RBF) for approximating the expensive simulation model. Once the RBF’s parameters are fitted, the algorithm selects multiple points to be evaluated simultaneously. The next point(s) to be evaluated in the expensive simulation are obtained based on their probability to attain a better result for the objective function. This approach represents a simplified oriented search. To evaluate the efficacy of this novel decision-making framework, a hypothetical multiproduct lignocellulosic biorefinery is globally optimized on its operational level. The obtained optimal points are compared with traditional optimization methods, e.g. Monte-Carlo simulation, and are evaluated for both proposed types of uncertainty calculated

How does technology pathway choice influence economic viability and environmental impacts of lignocellulosic biorefineries?

Background: The need for liquid fuels in the transportation sector is increasing, and it is essential to develop industrially sustainable processes that simultaneously address the tri-fold sustainability metrics of technological feasibility, economic viability, and environmental impacts. Biorefineries based on lignocellulosic feedstocks could yield high-value products such as ethyl acetate, dodecane, ethylene, and hexane. This work focuses on assessing biochemical and biomass to electricity platforms for conversion of Banagrass and Energycane into valuable fuels and chemicals using the tri-fold sustainability metrics. Results: The production cost of various products produced from Banagrass was $1.19/kg ethanol,$1.00/kg ethyl acetate, $3.01/kg dodecane (jet fuel equivalent),$2.34/kg ethylene and $0.32/kW-h electricity. The production cost of different products using Energycane as a feedstock was$1.31/kg ethanol, $1.11/kg ethyl acetate,$3.35/kg dodecane, and $2.62/kg ethylene. The sensitivity analysis revealed that the price of the main product, feedstock cost and cost of ethanol affected the profitability the overall process. Banagrass yielded 11% higher ethanol compared to Energycane, which could be attributed to the differences in the composition of these lignocellulosic biomass sources. Acidification potential was highest when ethylene was produced at the rate of 2.56 × 10−2 and 1.71 × 10−2 kg SO2 eq. for Banagrass and Energycane, respectively. Ethanol production from Banagrass and Energycane resulted in a global warming potential of − 12.3 and − 40.0 g CO2 eq./kg ethanol. Conclusions: Utilizing hexoses and pentoses from Banagrass to produce ethyl acetate was the most economical scenario with a payback period of 11.2 years and an ROI of 8.93%, respectively. Electricity production was the most unprofitable scenario with an ROI of − 29.6% using Banagrass/Energycane as a feedstock that could be attributed to high feedstock moisture content. Producing ethylene or dodecane from either of the feedstocks was not economical. The moisture content and composition of biomasses affected overall economics of the various pathways studied. Producing ethanol and ethyl acetate from Energycane had a global warming potential of − 3.01 kg CO2 eq./kg ethyl acetate

Continuous Biochemical Processing: Investigating Novel Strategies to Produce Sustainable Fuels and Pharmaceuticals

Biochemical processing methods have been targeted as one of the potential renewable strategies for producing commodities currently dominated by the petrochemical industry. To design biochemical systems with the ability to compete with petrochemical facilities, inroads are needed to transition from traditional batch methods to continuous methods. Recent advancements in the areas of process systems and biochemical engineering have provided the tools necessary to study and design these continuous biochemical systems to maximize productivity and substrate utilization while reducing capital and operating costs. The first goal of this thesis is to propose a novel strategy for the continuous biochemical production of pharmaceuticals. The structural complexity of most pharmaceutical compounds makes chemical synthesis a difficult option, facilitating the need for their biological production. To this end, a continuous, multi-feed bioreactor system composed of multiple independently controlled feeds for substrate(s) and media is proposed to freely manipulate the bioreactor dilution rate and substrate concentrations. The optimal feed flow rates are determined through the solution to an optimal control problem where the kinetic models describing the time-variant system states are used as constraints. This new bioreactor paradigm is exemplified through the batch and continuous cultivation of β-carotene, a representative product of the mevalonate pathway, using Saccharomyces cerevisiae strain mutant SM14. The second goal of this thesis is to design continuous, biochemical processes capable of economically producing alternative liquid fuels. The large-scale, continuous production of ethanol via consolidated bioprocessing (CBP) is examined. Optimal process topologies for the CBP technology selected from a superstructure considering multiple biomass feeds, chosen from those available across the United States, and multiple prospective pretreatment technologies. Similarly, the production of butanol via acetone-butanol-ethanol (ABE) fermentation is explored using process intensification to improve process productivity and profitability. To overcome the inhibitory nature of the butanol product, the multi-feed bioreactor paradigm developed for pharmaceutical production is utilized with in situ gas stripping to simultaneously provide dilution effects and selectively remove the volatile ABE components. Optimal control and process synthesis techniques are utilized to determine the benefits of gas stripping and design a butanol production process guaranteed to be profitable