1.1. Background and Motivation
Countries all over the world are focusing on renewable energy resources as an attractive option for achieving future energy security. Access to clean and non-polluting energy has been defined as a Sustainable Development Goal (SDG). In this context, many countries are promoting strategies to diversify the power generation mix considering clean energy projects to mitigate the climate change originated by greenhouse gas emissions from fossil fuel consumption. To achieve this purpose, one of the main energy resources available is the biomass. Biomass is defined as “a biological material derived from living or recently living organisms,” There are three main biomass types—(1) first-generation biomass, which includes edible crops, such as corn and sugarcane; (2) second-generation biomass, which includes wood, organic waste, food waste, and specific biomass crops residues; and (3) third-generation biomass, mainly based on algae [1
]. In this paper, we present a model of supply-chain design for sugarcane crop residues in the sugar industry. Currently, the sugar industry has been participating in power cogeneration projects. Usually, in these projects, sugar factories use a mix of fuels, mainly bagasse—a by–product of the production process—and coal. There is an opportunity to include part of the sugarcane crop residues (SCRs) originated in crop activities in the mix. The use of SCRs arises as a possible substitute for the coal used in boilers. Authors like those of reference [2
] have made a complete analysis of the state of research and trends in biomass for renewable energy from 1978 to 2018, focusing on the current situation and future trends. This information is very useful for making decisions about the future of scientific policy in the field of renewable energy projects.
The use of agricultural crop residues in electrical co-generation processes has been widely discussed in the literature. Some authors, such as those of reference [3
], systematically describe energy needs and targets, biofuel feedstocks, conversion processes, and provide a comprehensive review of biomass supply chain (BSC) design and modeling. In reference [4
], the authors describe the key challenges and opportunities in the modeling and optimization of biomass-to-bioenergy supply chains. The optimization process of a biomass supply chain is essential to overcome barriers and uncertainties that may inhibit the development of a sustainable and competitive bioenergy market. In reference [5
], the authors give an overview of the optimization techniques and models, focusing on decisions regarding the design and management of the upstream segment of the biomass-for-bioenergy supply chain. Another research approach is related to the integration of different optimization objectives in biomass supply chain models. There is a recent tendency to integrate economic, environmental, and social aspects in the evaluation and optimization of biomass supply chains. Some authors (for example, those of reference [6
]) have reviewed the studies that assessed or optimized economic, social, and environmental aspects of forest biomass supply chains for the production of bioenergy and bio-products.
The structure of this paper is as follows: In Section 1.2
, we present a literature review of bioenergy supply chain optimization decisions. In Section 2
, the optimization model is explained, taking into account the parameters, variables, objective functions, and constraints. Section 3
and Section 4
present the results and discussion. Section 5
presents conclusions and future research options. The Julia code of the model is included as an appendix.
1.2. Literature Review—Bioenergy Supply Chain Optimization
For tropical and subtropical countries, sugarcane is one of the most important crops, from economic, social, and environmental points of view. The area and productivity of sugarcane differ from one country to another. It is estimated that this crop is grown in 200 countries, with Asia being the region with the greatest contribution to world production (44%), while South America contributes 34%, including Brazil, the largest sugarcane producer in the world, accounting for 25% of total world production [7
]. Ninety-two percent of the sugarcane harvested in the world is directed toward sugar production. Sugar production and trade flows are concentrated in large producers, exporters, and importers, such as Brazil, India, Thailand, the European Union, Australia, and China [8
There are different studies about the use of sugarcane crop residues in the bioenergy production. According to reference [9
], sugarcane crop residues can be used as raw material in the production of bioenergy or biofuels. The use of these resources can avoid environmental problems and concerns. Each country has its unique set of crop residues that can be used for the generation of biofuels or bioenergy. In reference [10
], the authors assess the bioelectricity potential from ecologically available sugarcane straw in the state of Sao Paulo (Brazil) considering the spatial distribution of sugarcane fields, the spatial variation of sugarcane yield, the location, and the milling data of each mill. The bioelectricity potential from ecologically available sugarcane straw is estimated to be between 18.7 and 45.8 TWh in Sao Paulo, equal to 22–37% of the electricity demand. Reference [11
] presents a techno-economic analysis of upgraded sugarcane bio-refineries in Brazil, aiming at utilizing surplus bagasse and cane trash for electricity and/or ethanol production. This study investigates the trade-off on sugarcane biomass use for energy production—bioelectricity versus 2G ethanol production. In references [12
], the authors value the cogeneration potential of the sugar industry as a new business model for sugar mills.
The main activities in the sugarcane industry are crop, harvest, and factory (sugar mill plant and distilleries). SCR is a by-product of the crop activities (Figure 1
). SCRs are not used in the amount required by current circumstances. It has been estimated that, for each ton of cane, between 70 and 80% are clean stems, and the rest is what is called crop residues (green leaves, dried leaves, buds, etc.). The SCR can be used for different purposes, such as soil improvement, animal feed, energy generation, and other applications (boards, binders for construction, handicraft products, etc.). These residues, obviously, constitute a renewable energy source, but their use depends largely on aspects such as collection, transport, storage, and technological processes to transform them.
The collection process, traditionally, can be done in two places—in collection centers (cleaning stations) or in the field itself. Among the aspects related to residue handling, one of the most important is transport due to its technical-economic impact [15
]. Agricultural residue is a low density material, and the amount that can be moved is limited by volume and not by weight. Therefore, the use of all available space is essential for transport economics. For this, some mechanical treatments have been carried out that can eliminate the geometric heterogeneity of the residue and make compaction possible for later use. Finally, some authors warn of the need to keep 50% of the residual in the field to preserve appropriate soil conditions [16
In Colombia, it is estimated that the cultivation of sugarcane has a production potential of SCR per ton of cane close to 25%. This availability can be affected by factors such as: variety of cane planted, crop agro climatic conditions, agronomic practices and the harvest types, among others [17
presents the potential production of SCR in the state of Valle del Cauca, depending on the crop variety.
Under this structure and according to the amount of cane milled by the sector (25 million tons in 2018), approximately 6.25 million tons of SCR could be generated. However, total SCR that would actually be available is reduced to approximately 1.8 million tons per year due to crop type and the material that should be left in the field for soil conservation.
The SCR is an important energy resource for the sugar sector, but its use requires overcoming some technical challenges. One of these is its low energy content compared to traditional fossil fuels (higher calorific value: coal 25,983.7 KJ/kg; SCR 16,963.52 KJ/kg). Its low density (100 kg/m3
), added to its energy characteristics, makes the biomass volumes necessary to meet the energy need for a possible replacement of the coal used in the boilers of the sector significantly higher. In that sense, 1.53 tons of SCR are required to replace the calorific output of a ton of coal. This substitution has the clear benefit of reducing the carbon footprint, since 1 ton of coal would generate 2254 kg of equivalent CO2
, while 1.53 tons of SCR generate 53.7 kg of equivalent CO2
The use of mathematical models to analyze the performance of the bioenergy supply chain has been widely studied in the literature. [19
] Some authors, such as those of references [4
], describe the key challenges and opportunities in modeling and optimization of biomass-to-bioenergy supply chains. According to references [5
], one of the most important barriers to the development of a strong bioenergy sector is the cost of the biomass supply chain—specifically, the cost of SCR preparation, transport, and storage. In this context, the SCR supply chain cost competes directly with fossil fuel cost (coal). To overcome all these barriers and uncertainties, biomass supply chain optimization is essential.
Optimization decisions usually refer to the choice of highly productive non-food crops; the coordination of transportation, pre-treatment and storage at operational, tactical and strategic level; and the use of advanced efficient biomass-to-bioenergy conversion technologies to enable relevant reductions in environmental and biomass production costs [5
A first step in the study of bioenergy supply chain optimization models is to build a taxonomy of the main optimization decisions for operational, tactical and strategic level [23
]. Decisions related to biomass supply chain to energy conversion usually consider activities such as preparing and conditioning, transport, storage, and processing to energy generation. Normally, these decisions are taken at different levels. Some decisions can be strategic, such as technology type, kind of biomass, contracting, selection of transportation mode, supply network design, inventory levels, etc. At the tactical level the set of decisions includes storage conditions, vehicle routing, and harvest scheduling. The main biomass supply chain optimization decisions considered are presented in Figure 2
On the other hand, when we consider the strategies to model biomass supply chain, usually we find stochastic, deterministic, hybrid, and IT-driven models. In the field of deterministic models, we can develope single and multiple objective models. The techniques used to solve the deterministic models include linear, mixed, integer linear and non-linear programming.
Specifically, in the sugar agro-industrial production chain, different strategies have been developed for the use of the biomass produced in the cultivation process. An important part of the total fiber produced by the sugarcane can be used as a renewable energy resource. The use of sugarcane crop residues as a substitute fuel in electric cogeneration systems begins to become a topic with great research potential in an effort to seek power generation systems with less environmental impact. Reference [24
] presents an analysis of the potential of electricity generation from sugarcane crop residues in South Africa.
Some authors, like those of reference [25
], propose a multi-objective integer linear programming optimization model to choose sugarcane varieties to minimize costs in the use of crop residue and simultaneously maximize the energy balance in such a process. In that paper, the authors considered different logistics costs. First, the cost to collect, compact, and load the truck with residual biomass of different sugarcane variety. In addition to these costs, the model considered included the fixed and variable costs associated with the movement of the cargo vehicles. The inclusion of sugarcane variety in the model introduces another dimension related with different quantities of SCR production.
Other papers in which information regarding decision optimization models associated with the sugarcane supply chain can be found include reference [26
], which proposed a stochastic optimization model for sugarcane supply chain planning integrating sowing, growing, and harvesting operations. Reference [27
] presented a model to improve the integration between harvest decisions and transportation decisions in sugar industry supply chain in Australia. Finally, in references [28
], the authors proposed a decision support systems for the integration of supply chain decision in the sugar industry. The model developed was applied in the Cuban sugar industry.