Mathematical Optimization Models in the Sugarcane Harvesting Process

Over the past few decades, due to environmental and economic factors, the sugarcane has been considered a versatile and important plant to the several countries. The energy-sugar-ethanol agro-industries are seeking to take advantage of all its material, with the main products produced being renewable energy, sugar and ethanol. In this chapter, we propose to present a review of the important works that use mathematical and computational tools, aiming to optimize the sugarcane harvesting, in the past 30 years

Risk-conscious optimization model to support bioenergy investment in the Brazilian sugarcane industry

The past decades have seen a diversification of the sugarcane industry with the emergence of new technology to produce bioenergy from by-product and waste process streams. Given Brazil’s ambitious goal of reducing green-house gas emissions by over 40% below 2005 levels by 2030, it is of paramount importance to develop reliable decision-making systems in order to stimulate investment in these low-carbon technologies. This paper seeks to develop a more accurate optimization model to inform risk-conscious investment decisions for bioenergy generation capacity in sugarcane mills. The main objective is for the model to enable a better understanding of how Brazilian government policies, such as the electricity price in the regulated market, may impact these investments, by taking into account the uncertainty in sugar, ethanol and spot electricity markets and the interdependency between production and investment decisions in terms of saleable product mix. The proposed methodology combines portfolio optimization theory with superstructure process modeling and it relies on simple surrogates derived from a detailed sugarcane plant simulator to retain computational tractability and enable scenario analysis. The case study of an existing sugarcane plant is used to demonstrate the methodology and illustrate how the model can assist decision-makers. In all of the scenarios assessed, the model recommends investment in extra bioelectricity capacity via the anaerobic digestion of vinasse but advises against investment in second-generation ethanol production via the hydrolysis of surplus bagasse. Furthermore, the decision to upgrade the cogeneration system with a condensation turbine is highly sensitive to the electricity price practiced in the regulated market, capacity constraints on the sugar-ethanol mix, and the accepted level of risk. Another key insight drawn from the case study is that recent market conditions have favored a production focused on the sugar business, making it challenging for policy-makers to create attractive scenarios for biofuels. Long-term electricity contracting appears to be the main hedging strategy for de-risking other products and investments in the sugarcane business, provided it is priced adequately

Modeling and optimization of the palm oil (Elaeis guineensis) supply chain in Colombia

The aim of this research is to develop a quantitative tool that supports decision-makers in the strategic planning of supply chains (SC). The problem to be solved consists in determining the optimal configuration of the palm oil SC, including decisions associated to the number, location and capacity of all the facilities of the SC in a given country; its expansion policy in the planning horizon, means of transportation, production rates, material flow, waste management, and its potential environmental impact. Bearing this in mind, two mathematical models are presented to address this problem. The first one is a mixed integer linear programming (MILP) model applied to the oil palm industry in Colombia that aims to maximize the net present value of its SC in a specific planning horizon. On the other hand, the second model solves a multi-objective optimization (MOO) MILP problem. It combines the first model with the Life Cycle Assessment (LCA) methodology to optimize the palm oil SC in Colombia. The MOO model aims at maximizing the economic benefit of this SC and simultaneously minimizing its environmental impact (measured in “eco-points”). The MOO problem was solved using the epsilon constraint method. Pareto optimal solutions provide valuable information for the optimal design and configuration of the palm oil SC, in particular the compensations or trade-offs resulting from economic profit, and its environmental impact. The solutions obtained through this model show a more rational distribution of productive units, including the establishment of renewable power plants.DoctoradoDoctor en Ingeniería Industria

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

Integrating bioprocesses into industrial complexes for sustainable development

The objective of this research is to propose, develop and demonstrate a methodology for the optimal integration of bioprocesses in an existing chemical production complex. Chemical complex optimization is determining the optimal configuration of chemical plants in a superstructure of possible plants based on economic, environmental and sustainable criteria objective function (triple bottomline) and solves a mixed integer non linear programming problem. This research demonstrated the transition of production of chemicals from non-renewable to renewable feedstock. A conceptual design of biochemical processes was converted to five industrial scale designs in Aspen HYSYS® process simulator. Fourteen input-output block models were created from the designs based on the mass and energy relations. A superstructure of plants was formed by integrating the bioprocess models into a base case of existing plants in the lower Mississippi River corridor. Carbon dioxide produced from the integrated complex was used for algae oil and new chemicals production. The superstructure had 978 equality constraints, 91 inequality constraints, 969 continuous variables and 25 binary variables. The optimal solution gave a triple bottomline profit of $1,650 million per year from the base case solution of$854 million per year (93% increase). Raw material costs in the optimal solution decreased by 31% due to the exclusion of the costly ethylbenzene process. The utility costs for the complex increased to $46 million per year from$12 million per year. The sustainable costs to the society decreased to $10 million per year from$18 million per year (44% decrease). The bioprocesses increased the pure carbon dioxide sources to 1.07 million metric tons per year from 0.75 million metric tons per year for the base case (43% increase). The pure carbon dioxide vented to the atmosphere was reduced to zero in the optimal structure from 0.61 million metric tons per year (100% decrease) by consumption in the complex. The methodology can be used by decision makers to evaluate energy efficient and environmentally acceptable plants and have new products from greenhouse gases. Based on these results, the methodology could be applied to other chemical complexes in the world for reduced emissions and energy savings

Novel approach for integrated biomass supply chain synthesis and optimisation

Despite looming energy crises, fossil resources are still widely used for energy and chemical production. Growing awareness of the environmental impact from fossil fuels has made sustainability one of the main focuses in research and development. Towards that end, biomass is identified as a promising renewable source of carbon that can potentially replace fossil resources in energy and chemical productions. Although many researches on converting biomass to value-added product have been done, biomass is still considered underutilised in the industry. This is mainly due to challenges in the logistic and processing network of biomass. An integrated biomass supply chain synthesis and optimisation are therefore important. Thus, the ultimate goal of this thesis is to develop a novel approach for an integrated biomass supply chain. Firstly, a multiple biomass corridor (MBC) concept is presented to integrate various biomass and processing technologies into existing biomass supply chain system in urban and developed regions. Based on this approach, a framework is developed for the synthesis of a more diversified and economical biomass supply chain system. The work is then extended to consider the centralisation and decentralisation of supply chain structure. In this manner, P-graph-aided decomposition approach (PADA) is proposed, whereby it divides the complex supply chain problem into two smaller sub-problems – the processing network is solved via mixed-integer linear programming (MILP) model, whereas the binaries-intensive logistic network configuration is determined through P-graph framework. As existing works often focus on supply chain synthesis in urban regions with well-developed infrastructure, resources integrated network (RIN) – a novel approach for the synthesis of integrated biomass supply chain in rural and remote regions is introduced to enhance rural economies. This approach incorporates multiple resources (i.e. bioresources, food commodities, rural communities’ daily needs) into the value chain and utilises inland water system as the mode of transport, making the system more economically feasible. It extends the MBC approach for technology selection and adopts vehicle routing problem (VRP) for inland water supply and delivery network. To evaluate the performance of the proposed integrated biomass supply chain system, a FANP-based (fuzzy analytical network process) sustainability assessment tool is established. A framework is proposed to derive sustainability index (SI) from pairwise comparison done by supply chain stakeholders to assess the sustainability of a system. Fuzzy limits are introduced to reduce uncertainties in human judgment while conducting the pairwise comparison. To design a sustainable integrated biomass supply chain, a FANP-aided, a novel multiple objectives optimisation framework is proposed. This approach transforms multiple objective functions into single objective function by prioritising each of the objective through the FANP framework. The multiple objectives are then normalised via max-min aggregation to ensure the trade-off between objectives is performed on the same scale. At the end of this thesis, viable future works of the whole programme is presented for consideration

Cuban energy system development – Technological challenges and possibilities

This eBook is a unique scientific journey to the changing frontiers of energy transition in Cuba focusing on technological challenges of the Cuban energy transition. The focus of this milestone publication is on technological aspects of energy transition in Cuba. Green energy transition with renewable energy sources requires the ability to identify opportunities across industries and services and apply the right technologies and tools to achieve more sustainable energy production systems. The eBook is covering a large diversity of Caribbean country´s experiences of new green technological solutions and applications. It includes various technology assessments of energy systems and technological foresight analyses with a special focus on Cuba

A MULTIDISCIPLINARY TECHNO-ECONOMIC DECISION SUPPORT TOOL FOR VALIDATING LONG-TERM ECONOMIC VIABILITY OF BIOREFINING PROCESSES

Increasing demand for energy and transportation fuel has motivated researchers all around the world to explore alternatives for a long-term sustainable source of energy. Biomass is one such renewable resource that can be converted into various marketable products by the process of biorefining. Currently, research is taking strides in developing conversion techniques for producing biofuels from multiple bio-based feedstocks. However, the greatest concern with emerging processes is the long-term viability as a sustainable source of energy. Hence, a framework is required that can incorporate novel and existing processes to validate their economic, environmental and social potential in satisfying present energy demands, without compromising the ability of future generations to meet their own energy needs. This research focuses on developing a framework that can incorporate fundamental research to determine its long-term viability, simultaneously providing critical techno-economic and decision support information to various stakeholders. This contribution links various simulation and optimization models to create a decision support tool, to estimate the viability of biorefining options in any given region. Multiple disciplines from the Process Systems Engineering and Supply Chain Management are integrated to develop the comprehensive framework. Process simulation models for thermochemical and biochemical processes are developed and optimized using Aspen Engineering Suite. Finally, for validation, the framework is analyzed by combining the outcomes of the process simulation with the supply chain models. The developed techno-economic model takes into account detailed variable costs and capital investments for various conversion processes. Subsequently, case studies are performed to demonstrate the applicability of the decision support tool for the Jackson Purchase region of Western Kentucky. The multidisciplinary framework is a unique contribution in the field of Process Systems Engineering as it demonstrates simulation of process optimization models and illustrates its iterative linking with the supply chain optimization models to estimate the economics of biorefinery from multi-stakeholder perspective. This informative tool not only assists in comparing modes of operation but also forecasts the effect of future scenarios, such as, utilization of marginal land for planting dedicated energy crops and incorporation of emerging enzymatic processes. The resulting framework is novel and informative in assisting investors, policy makers and other stakeholders for evaluating the impacts of biorefining. The results obtained supports the generalizability of this tool to be applied in any given region and guide stakeholders in making financial and strategic decisions