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: Optimal Planning of Biomass Co-Firing Networks with Biochar-Based Carbon Sequestration

KB Aviso,1-3 [1] [2] JLG San Juan,2 CL Sy2-3 and RR Tan1-3


Climate change has emerged as one of the world's most critical environmental issues. According to the IPCC (2018), net global greenhouse gas (GHG) emissions need to be reduced to zero by the mid- 21st Century in order to keep mean temperature rise by 2100 to a manageable level of about 1.5 °C. Furthermore, commitments made to GHG emissions under the Paris Accord result in a strong need for the deployment of low-carbon technologies in order for such cuts to be realized. Strategies include the increased use of low-carbon energy sources, such as biomass, and the large-scale deployment of carbon dioxide removal (CDR) or negative emissions technologies (NETs) which can achieve significant cuts in the release of GHGs and potentially stabilize climate in the coming decades (Haszeldine et al., 2018). Shifting to cleaner power generation will also complement the mass electrification of urban motor vehicles (Erickson, 2017). Examples of NETs are direct air capture (DAC). bioenergy with CO, capture and storage (BECCS), and biocliar-based carbon sequestration (McGlashan et al., 2012). The potential scale and technological maturity levels of NETs were assessed by McLaren (2012). More recently, two review papers surveyed the research status (Minx et al., 2018) and techno-economics (Fuss et al., 2018) of different NETs.

Biocliar-based systems offer an alternative means to achieve negative emissions. In such systems, biochar is applied to soil to achieve the net removal of carbon from the atmosphere. Thus, the carbon that was originally in atmospheric CO, is fixed via photosynthesis into plant biomass. The latter is subsequently converted via thennochemical processing (i.e., pyrolysis or gasification) into biochar, whose carbon content is in a predominantly chemically stable (recalcitrant) form. Application of the resulting biocliar to soil thus results in permanent sequestration of this recalcitrant carbon (Woolf et al., 2010). Such systems are naturally compatible with other biomass-based energy systems, and additional benefits may accrue from modification of soil biota which further reduces the release of GHGs (He et al..

2017). Ail advantageous feature of using biochar is the potential to produce useful energy while storing carbon (Smith. 2016). Planning of integrated, biochar-based carbon management networks (CMNs) can be facilitated through the use of computer-aided process engineering (CAPE) or process systems engineering (PSE) tools (Belmonte et ah, 2017). The prospect of integrating biochar-based CMNs with biomass co-firing in power plants was recently proposed by Dang et al. (2015), who suggested a novel scheme to co-fire pyrolysis bio-oil with coal and to apply the biochar to soil; initial life-cycle analysis (LCA) showed significant potential to reduce CO, emissions. However, their work did not consider the optimization of such systems.

Co-firing of biomass with fossil fuels is a technologically mature approach to reducing GHG emissions, and is already widely used throughout the world (Roni et ah, 2017). The feasibility of using biomass, such as agricultural waste (e.g., com stover, rice straw, etc.), as an energy source for co-firing in modified existing coal power plants is well-established in the literature. Co-firing gives an immediate and practical mode of reducing coal usage and the associated GHG emissions. Furthermore, co-firing enables biomass to be used in existing coal power plants, instead of having to build dedicated biomass- fired plants (Madauayake et ah, 2017). Co-firing biomass with coal is an attractive alternative because biomass can be integrated into existing coal-fired power plants’ fuel storage and handling systems with only relatively minor retrofits, and allows for flexible operation with diverse feedstocks (Dundar et ah,

2016). This flexibility leads to reduced techno-economic risk compared to the alternative of building stand-alone biomass-fired power plants that are entirely dependent on a potentially unstable supply of fuel. However, co-firing is usually limited to 10% biomass on a fuel energy basis, as higher rates of utilization may have adverse effects on plant equipment. Furthermore, it has been argued that high co-firing rates are detrimental due to the GHG emissions penalty that result from handling biomass, which has a lower energy density than coal (Miedema et ah. 2017). Biomass co-firing also improves the net energy and emissions balance because the combustion of biomass residues, such as rice straw, makes use of less energy and releases less emissions when the upstream coal supply chain (i.e., mining and transportation operations) is considered in the analysis (Shafie et ah, 2013). Co-firing systems in existing coal-fired power plants can be implemented using three possible configurations, namely, direct, indirect, and parallel co-firing (Agbor et ah, 2014). Direct co-firing systems are characterized by the use of a single common boiler burning a blend of coal and biomass, or via separate burners for biomass and coal providing heat to a common boiler. Indirect co-firing uses gasification to convert biomass into syngas, which can then be used as the secondary fuel as in direct co-firing systems; in pyrolysis-based systems, the liquid fraction (bio-oil) can also be used as fuel along with the syngas. The residual solid biochar is available for carbon sequestration. On the other hand, parallel co-firing systems bum coal and biomass in separate boilers, which then feed into a common turbine. In such schemes, the solid biochar residue can be separated and applied to soil to achieve further GHG offsets. Titus, indirect co-firing can potentially achieve negative emissions for the biomass fraction of the power plant fuel input, even if total emissions remain positive due to the combustion of coal (Dang et al., 2015).

PSE tools can be used to facilitate planning sustainable supply chains (Cabezas et al., 2018), particularly for biomass logistics (Atashbar et al., 2018). Novel approaches to the optimization of biomass supply chains have been proposed to account for the inherently dispersed nature of biomass resources. Lim and Lam (2016) proposed a biomass element life cycle analysis (BELCA) approach that takes into account variations in biomass feedstock composition and properties to identify opportunities for blending and for matching with downstream demands. Different supply chain configurations, taking into account factors such as feedstock supply, production scale, transport distance, and the use of intermediate depots, were subjected to economic and energetic analysis by Ng and Maravelias (2017). Ng et al. (2018) then developed a framework for biomass supply chain optimization. The framework includes preprocessing to generate a simplified superstructure, and incorporates supply chain performance metrics within the resulting optimization model. It is notable that most of the literature on co-firing supply chain optimization focuses on direct co-firing (Atashbar et al., 2018). There har e been studies that proposed a biomass co-firing supply chain optimization model that minimized both the cost and emissions, such as Mohd Idris et al. (2018) for oil palm biomass and Griffin et al. (2014) for mixed biomass. Perez-Fortes et al. (2014) developed a mixed integer linear programming (MILP) model to decide on the optimal pretreatment of biomass. The dominance of direct co-firing over indirect and parallel co-firing is attributable to the need for relatively invasive plant retrofits (Madanayake et al., 2017). This work addresses the gap in the literature by developing a two-layer supply chain model for indirect biomass co-firing coupled with biochar-based carbon sequestration.

Belmonte et al. (2017) argue that the use of PSE is essential to the proper large-scale deployment of biochar-based systems. The optimal synthesis of biochar-based CMNs was first addressed by Tan (2016), who developed a multi-period MILP to allocate biochar to different soils acting as biochar sinks. The model accounts for storage capacities and quality (contaminant) requirements. A bi-objective extension of this initial model was recently developed (Belmonte et al., 2018). Other approaches, based on pinch analysis (Tan et al., 2018) and the P-graph framework (Aviso et al., 2019), were recently developed for optimizing biochar-based CMNs. Despite recent developments in the PSE literature on mathematical models to aid in the planning of biochar-based CMNs, as well as an extensive body of literature on biomass supply chains, no spatially explicit optimization models have yet been reported that combine these two aspects in the context of low-carbon electricity generation. A natural extension to the works of Tan (2016) and Belmonte et al. (2018) is to use a supply chain perspective for biocliar-based carbon sequestration.

In this chapter, a novel MILP model for optimal planning of integrated systems that combine upstream biomass allocation networks for co-firing in power plants with the biochar allocation networks for carbon sequestration is developed. The model is able to take into account biomass supply, power plant capacities and biochar application limits to enable rational system-level planning, which will be essential to enable full-scale implementation of such systems. It is assumed that the relevant data for model calibration are known a priori; acquisition of such data on a large scale remains a major research challenge (Tan, 2019) that is outside of the scope of this work. The rest of the chapter is organized as follows: Section 2 gives the formal problem statement that specifies model inputs and outputs. The MILP model formulation is then described in section 3. An illustrative case study is then solved in section 4 to demonstrate the model capabilities; the optimal and near-optimal solutions are analyzed to show how multiple options can be used to facilitate decision-making. Finally, conclusions and prospects for future work are given in section 5.

  • [1] 1 Chemical Engineering Department, De La Salle University, Manila, Philippines. - Industnal Engineenng Department, De La Salle University, Manila, Philippines. 1 Center for Engineering and Sustainable Development Research, De La Salle University, Manila, Philippines.
  • [2] Corresponding author: This email address is being protected from spam bots, you need Javascript enabled to view it
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