Biomass Supply Chain Network and Circular Economy

The main steps of biomass supply chain network include collection of feedstocks (i.e. wastes and by-products both from production plants and usage), pre-treatment and conversion processes, upgrading/purification processes and finally distribution of the products. Therefore, the design of a supply-chain network aims at the minimum cost of the whole chain.

The design of supply chain network determines the capacities and locations of conversion processes and upgrade/purification plants. The network modeling involves strategical decisions, input information, the objective function, and tactical and operational aspects of implementation (Yue et al., 2011). The strategical decisions involve the selection of biomass types and conversion technologies as well as modes of transportation. The input information includes the biomass activity site locations, the feedstock amounts from those sites, product yields, operation costs per unit product, fuel consumption and transportation costs per unit distance, and the demand site locations and the demand amounts in those sites. Then, the objective usually covers the economic aspect of the network, on the constraints of locations and amounts of available feedstock and product demand.

For instance, Marvin et al. (2012) used maximum net present value as the objective when designing a biomass-to-ethanol supply chain network. In addition, Kim et al. (2011) used maximum profit for a biomass-to-liquid fuels network, and Akgul et al. (2012a) used minimum cost as the single objective when designing bioethanol supply chain network. This is called single-objective optimization: one objective function with respect to the locations and capacities of the processing plants. The single-objective optimization models are easier to solve and require less computation than multi-objective models.

The multi-objective optimization involves the compromise between two contradicting objectives, e.g. minimizing the greenhouse gas emissions and maximizing the profit. For example, You and Wang (2011) used minimum greenhouse gas emissions and minimum annualized costs as the multiple-objective when modelling biomass-to-liquid fuels network. Another approach is to define emission constraints as well in the single-objective model. Akgul et al. (2012b) inserted emission limits as constraints and used minimum daily cost of the whole chain when designing the bioethanol supply chain network defined by Akgul et al. (2012a). Sharma et al. (2013a) provided a detailed review of the studies about supply chain network, covering the aspects of objective functions, model types, network structures, and biomass and product types. The other important aspect is uncertainty of the conditions, e.g. the impact of weather. This aspect is involved through stochastic models (Awudu and Zhang, 2012; Sharma et al., 2013b).

112 3.1. Supply Chain Network Structures

The structure of a supply chain network involves a compromise between the operation costs and the transportation costs. The high capacity in plants would decrease the operation cost per unit product but increase the transportation costs of feedstock and vice versa. Therefore, different sturctures occur in supply chain networks, namely distributed, centralized and distributed-centralized as shown in Figure 5.

Figure 5. Centralized (top), distributed (middle) and distributed-centralized (bottom) supply chain network structures

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The distributed structure locates the complete processing from pre-treatment to purification at each biomass activity site. This eliminates the transportation costs of feedstock; however, the operation costs would increase, and the processes are likely to be economically unfeasible. In contrast, the centralized structure involves a single processing from pre-treatment to purification in large capacity, to where the feedstock is transferred from all the biomass sites. This structure provides reduced operation costs but increase the transportation costs sharply. In this regard, Bowling et al.

(2011) proposed the collection hubs for the transportation of feedstock from biomass activity sites to the centralized plant. However, despite the improvement, this type of network still results in very high transportation costs and spontaneous deterioration in case of long storage time. The transportation costs from hubs to the centralized plant can be reduced by processing the collected feedstock, i.e. the distributed-centralized structure. This structure addresses the issue of spontaneous deterioration as well.

The distributed-centralized structure is the balance between the other two structures: conversion processes receiving feedstock from few biomass activity sites and a centralized upgrade/purification process. The biomass conversion processes increase the energy density, thus reducing the costs of transportation to the centralized plant. For instance, Kim et al. (2011) compared centralized gasification followed by Fischer-Tropsch synthesis (the centralized structure) with distributed pyrolysis and centralized gasification followed by Fischer-Tropsch synthesis (the centralized structure). The optimum result was the distributed-centralized structure with respect to the profit. Furthermore, this structure was stated to be more robust against the variations in market demand and prices than the centralized structure (Kim et al., 2011). Similarly, You and Wang (2011) obtained the distributed-centralized structure as the optimum network when designing biomass-to-liquid biofuel supply chain. The distributed-centralized structure provides more distributed job opportunities and development in rural areas (Papendiek et al., 2012).

However, the studies on biomass supply chain network are limited to a single feedstock and/or product through a specified conversion process in the whole chain. For instance, ethanol was the only target product in the investigations by Akgul et al. (2012a, 2012b) and Marvin et al. (2012).

Similarly, Kim et al. (2011) and You and Wang et al. (2011) targeted only liquid biofuel. Instead, biomass enables multiple products, and biomass supply chain needs improvement through involving more feedstock. These issues can be addressed by more enhanced supply chain network structure and biomass conversion processes.

3.2. Sectoral Integration for Circular Economy

A biomass supply chain network should have the features of adaptability and flexibility (Yue et al., 2014). The product demand and prices can change based on the situation in the industrial sectors. In addition, the biomass feedstock can vary seasonally in quantity and quality as well. The supply chain network should be able to adjust the production amounts of various products and

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adapt the feedstock variations. This requires multi-feed-multi-product and flexible conversion processes (Özdenkçi et al., 2017). For instance, the hydrothermal process in Figure 4 can adjust lignin production by controlling the stream going to lignin recovery section. In addition, the other product can be switched between syngas and bio-oil in accordance with the demands to CHP, liquid fuels and other products that can be produced from syngas. Furthermore, the temperatures in gas separation units can also be adjusted in accordance with the desired use of syngas: e.g.

maximizing hydrogen content or miximizing the heating value in H2-rich gas stream. Regarding the feedstock, this process can utilize both solid and liquid streams. The PWO unit operates as simultaneous dissolution and partial oxidation in case of solid feedstock. There can be other multi-feed-multi-product amd flexible processes involved in the network design as well.

The sectoral integration structure is the enhanced version of distributed-centralized network structure as proposed by Özdenkçi et al. (2017). Figure 6 shows the conceptual structure of the sectoral integration network. As additional features, the sectoral integration involves pre-treatment at the biomass sites and multi-feed-multi-product conversion processes. The pre-treatment at biomass sites prepares the waste or byproduct streams as suitable raw materials for the regional biomass conversion processes. The regional conversion processes can produce CHP for the region and products to be used locally or to be upgraded at the centralized facility. The optimization model can result in more than one centralized plant and/or a centralized plant integrated to one of the regional conversion processes. This type of configuration benefits from the shared infrastructure. For instance, the centralized HDO plant can be integrated with a regional conversion process producing hydrogen, and bio-oil from other regional processes can be transferred to this centralized plant. Figure 7 illustrates a network with this configuration: only illustrative figure, not a network design for a real case. As further integration, CO2 produced in the regional conversion processes can be utilized in algae production, i.e. to be transported to another biomass activity site.

Then, algae can be used in hydrothermal processes, such as the one in Figure 4. Alternative use of algae includes animal feed and food supplement, pharmaceutical and plant protection products through supercritical algal extracts (Michalak et al., 2015).

Figure 6. The sectoral integration structure

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Figure 7. Illustrative example of plant locations through network design: brown filled circles for the biomass activity sites, blue rounded rectangles for the regional biomass conversion plants and green triangles for the centralized upgrading plants

The sectoral integration structure has several benefits:

• additional revenue and reduced financial risk via wider product spectrum

• secured feedstock supply for conversion processes from various sectors

• reducing the environmental impacts of all the involved sectors

• more evenly distributed job opportunities and development of rural areas

• more self-sufficiency to the countries with biomass activities

The sectoral integration has economic benefits distributed to the whole area of the network. The additional revenue is distributed to the rural areas as well as to the conversion processes. The wastes of biomass activities in rural areas become valuable raw materials for the regional conversion processes. The conversion processes have additional revenue as well via wider product spectrum. In addition, the risks are minimized through wide product spectrum and wide spectrum of feedstock supply. Even if the demand or price of one product reduce, the conversion process can switch partly or completely to other products and maintain the revenue. Similarly, no feedstock shortage occurs even if one biomass sector declines. The supply shortage may occur only if all the sectors declines simultaneously, which is very unlikely.

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Furthermore, the sectoral integration has environmental and social benefits as well. The environmental impacts of all involved sectors reduce simultaneously since all the waste streams are utilized in the conversion processes. In addition, the job opportunities are distributed to the rural areas as well in the sectoral integration structure. This would result in more evenly population distribution and hence improved management of social services in terms of those services reaching everywhere and everybody. Furthermore, a country applying the sectoral integration can be self-sufficient energy need and have major production of other biomass-derived products (such as food, animal feed, textile and chemicals) simultaneously. These productions would be sourced from the renewable biomass sources, thus eliminating the import of raw materials for energy and reducing the import of other chemicals.