DOI: 10.3303/CET24114095 Paper Received: 19 May 2024; Revised: 15 August 2024; Accepted: 23 November 2024 Please cite this article as: Ng W.P.Q., Ngu E.A.R., Ahmadbi N.F.B.H., Lam H.L., 2024, Biomass Processing Network Optimisation Using P- graph with Pareto Screening, Chemical Engineering Transactions, 114, 565-570 DOI:10.3303/CET24114095 CHEMICAL ENGINEERING TRANSACTIONS VOL. 114, 2024 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Petar S. Varbanov, Min Zeng, Yee Van Fan, Xuechao Wang Copyright Β© 2024, AIDIC Servizi S.r.l. ISBN 979-12-81206-12-0; ISSN 2283-9216 Biomass Processing Network Optimisation Using P-graph with Pareto Screening Wendy P. Q. Ng*,a, Elida A. R. Ngub, Nur Faakhirah binti Haji Ahmadbia, Hon Loong Lamb aUniversiti Teknologi Brunei, Jalan Tungku Link Gadong, BE1410 Brunei Darussalam bUniversity of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor, Malaysia peiqin.ng@utb.edu.bn A global movement is working towards emission reduction. Despite its availability, biomass is generally under- utilised due to various challenges, including financial factors. Convincing economic and environmental performances of biomass waste-to-wealth processing networks are needed to motivate investors and boost the implementation of biomass projects. In this work, the P-graph is combined with Pareto visualisation to optimise and screen biomass waste-to-wealth processing network to generate optimal models for investors’ selection. The selected optimal solution generates an annual gross profit of MYR 210 M and a total carbon emission of 69 kt CO2. This work aims to motivate the development of the biomass industry by providing a convincing statement to the palm industry and investors for investment and development. 1. Introduction Palm oil, renowned for its high melting point and stability at high temperatures, is widely used in the food industry as an edible oil. Palm oil segment has been dominating the global vegetable oils consumption market, at ~78 Mt/y (Statista, 2024). The huge production of palm oil also raises concerns about the significant volume of biomass generated by the palm oil industry, averaging at 9 t of biomass generated per t of crude palm oil produced (Loh and Choo, 2013). The palm biomass generated stems primarily from two sources: the oil palm plantation and the palm oil mill. The oil palm plantation yields Oil Palm Frond (OPF) and Oil Palm Trunk (OPT), while the palm oil mill produces Empty Fruit Bunch (EFB), Palm Kernel Shell (PKS), Palm Mesocarp Fibre (PMF), and Palm Oil Mill Effluent (POME). The large volume of palm biomass has created disposal and environmental issues due to poor waste management. In fact, these biomass can be processed into value-added products through various technologies. To tackle the waste disposal issues, the concept of circular economy (CE) can be applied. CE is defined as the system of regeneration that minimise waste generated by closing and extending the loops of supply chain and improving eco-efficiency technologies while maintaining and maximising its value in the economy based on three principles: eliminate waste and pollution, circulate products and materials at their highest value and regenerate nature. To achieve CE in palm oil industry, the concept of waste to wealth is the key to extend the loop of supply network by using palm biomass to produce valuable products through different technologies. If unutilised, palm biomass ends up in landfilling, which emits ~400 kg CO2e/t biomass (Nordahl et al., 2020). Different techniques are available for the synthesis and optimisation of biomass processing network. The application of P-graph in process synthesis was first used for mass exchange network synthesis (Lee and Park, 1996). The effectiveness of P-graph application in generating feasible structure compared to conventional mathematical programming approach was demonstrated. The application of P-graph was then extended for heat exchanger network (HEN) and biomass supply chain syntheses (How et al., 2019). These applications proved the capability of P-graph framework in process synthesis. As a graph-theoretical approach, P-graph displays a visual interface which enables users to construct the case study in an easier manner and allows audience without strong mathematical programming background to understand the case study easily. However, 565 P-graph is typically used for single objective optimisation. The combination of P-graph and Pareto visualisation enables another objective of different dimension to be included for solution screening. Palm biomass has great potential to be processed into value-added products and biomass energy, this approach mitigates environmental issues stemming from the unattended biomass in the palm oil industry. Many processing methods (e.g. enzymatic hydrolysis, fermentation, chemical extraction, etc.) have been developed and tested to produce various valuable products (Hau et al., 2022). The main challenge impeding the advancement of the biomass industry is the economic factor, manifested in additional capital requirement and the lack of convincing evidence regarding the profitability of biomass products. The initiative to evaluate the economic and environmental performances of the biomass waste-to-wealth supply network is crucial to stimulate the adoption of technologies for generating biomass-based products. A positive outcome from this evaluation could increase the possibility of attracting potential investors for biorefinery technologies and the deployment of biomass energy. This work aims to develop an optimisation model using the P-graph coupled with Pareto visualisation to assess the economic and environmental performances of a palm biomass supply network. The model identifies both the optimal and sub-optimal solutions across various process pathways using P-graph with Pareto visualization to screen the optimised solutions for further decision making. 2. Methodology The study initiates by establishing a biomass network and defining economic and environmental indicators. Then, a P-graph model is constructed, employing the Mixed Integer Linear Programming (MILP) and Accelerated Branch-and-Bound (ABB) algorithm within the P-graph framework. The P-graph superstructure is constructed by mapping the available biomass resources to the available biomass conversion technologies and ending with the production of respective biomass products from the processes. By utilising P-graph, the study ascertains feasible technologies and end-products for palm biomass processing, evaluating different pathways' feasibility and their corresponding economic and environmental impacts. The solutions are later visualised in Pareto chart for environmental performance screening. The economic performance of the biomass processing network is quantified by the gross profit of the pathway which includes biomass cost, transportation cost, operating expenditure (OPEX) and capital expenditure (CAPEX). In terms of environmental dimension, the environmental performance is quantified based on the total carbon emission. 2.1 Economic Dimension The biomass is sourced from palm oil mill and palm oil plantation. Raw material cost includes the biomass cost and the transportation cost, as shown in Eq(1). 𝐢𝑅𝑀 = πΆπ΅π‘–π‘œπ‘šπ‘Žπ‘ π‘  + πΆπ‘‡π‘Ÿπ‘Žπ‘›π‘ π‘π‘œπ‘Ÿπ‘‘ (1) Where 𝐢𝑅𝑀(MYR/t) is the total raw material cost, πΆπ΅π‘–π‘œπ‘šπ‘Žπ‘ π‘ (MYR/t) is the cost of biomass and πΆπ‘‡π‘Ÿπ‘Žπ‘›π‘ π‘π‘œπ‘Ÿπ‘‘(MYR/t) is the biomass transportation cost from the source to the processing plant. The biomass processing plant is assumed to be set up next to palm oil mill. EFB, PKS and POME are collected at palm oil mill and fed directly to the processing plant. OPF and OPT are collected in oil palm plantation, the average distance for biomass transportation from oil plantation to palm oil mill is 31 km (Arshad et al., 2019). The biomass transportation cost is determined using Eq(2). πΆπ‘‡π‘Ÿπ‘Žπ‘›π‘ π‘π‘œπ‘Ÿπ‘‘ = 𝐹𝐢 Γ— 𝐷 Γ— 𝐢𝐹𝑒𝑒𝑙/ 8 (2) Where πΆπ‘‡π‘Ÿπ‘Žπ‘›π‘ π‘π‘œπ‘Ÿπ‘‘ (MYR/t biomass) is the biomass transportation cost from the source to processing plant, 𝐹𝐢(L/km) is the fuel consumption rate of vehicle, 𝐷(km) is the distance from source to the plant, 𝐢𝐹𝑒𝑒𝑙(MYR/L) is the cost of diesel fuel. The cost function forms the economic indicator and is evaluated to be maximised: π‘ƒπ‘Ÿπ‘œπ‘“π‘–π‘‘ = πΆπ‘ƒπ‘Ÿπ‘œπ‘‘π‘’π‘π‘‘ βˆ’ 𝐢𝑅𝑀 βˆ’ 𝐢𝑂𝑃𝐸𝑋 βˆ’ 𝐢𝐢𝐴𝑃𝐸𝑋 (3) where π‘ƒπ‘Ÿπ‘œπ‘“π‘–π‘‘ (MYR/y) is the gross profit, πΆπ‘ƒπ‘Ÿπ‘œπ‘‘π‘’π‘π‘‘ (MYR/y) is the revenue received from product sales, 𝐢𝑅𝑀 (MYR/y) is the total raw material cost, 𝐢𝑂𝑃𝐸𝑋 (MYR/y) is the operational expenditure and 𝐢𝐢𝐴𝑃𝐸𝑋 (MYR/y) is the capital expenditure. 2.2 Environmental Dimension Total carbon emission, as shown in Eq(4), serves as the environmental indicator and measures the environmental performance of the biomass processing network. In the model, the environment indicator is input into P-graph by considering it as a product outlet for all the technologies. This allows P-graph model to evaluate the total carbon emission of the selected pathway. The carbon emission rate of each technology is determined with Eq(4). π‘‡π‘œπ‘‘π‘Žπ‘™ π‘π‘Žπ‘Ÿπ‘π‘œπ‘› π‘’π‘šπ‘–π‘ π‘ π‘–π‘œπ‘› = πΈπ‘ƒπ‘Ÿπ‘œπ‘π‘’π‘ π‘  + πΈπ‘‡π‘Ÿπ‘Žπ‘›π‘ π‘π‘œπ‘Ÿπ‘‘ + πΈπ‘ˆπ‘‘π‘–π‘™π‘–π‘‘π‘¦ (4) 566 where π‘‡π‘œπ‘‘π‘Žπ‘™ π‘π‘Žπ‘Ÿπ‘π‘œπ‘› π‘’π‘šπ‘–π‘ π‘ π‘–π‘œπ‘› (t CO2/y) is the total carbon emission, πΈπ‘ƒπ‘Ÿπ‘œπ‘π‘’π‘ π‘  (t CO2/y) is the carbon emitted by the process, πΈπ‘‡π‘Ÿπ‘Žπ‘›π‘ π‘π‘œπ‘Ÿπ‘‘ (t CO2/y) is the carbon emitted during the transportation of biomass and πΈπ‘ˆπ‘‘π‘–π‘™π‘–π‘‘π‘¦ (t CO2/y) is the indirect carbon emitted considering the consumption of electricity generated using fossil fuel. 2.3 Demonstration Case Study A demonstration case study is developed based on the annual production rate of an operating palm oil company in Malaysia. In this study, biomass from the oil palm plantation, i.e. OPF and OPT, and biomass from the palm oil mill, i.e. PKS, EFB and POME, are considered as sources of biomass. The integrated palm biomass supply network is designed to produce bioethanol, biochar, biooil, syngas and bio-methane through different technologies. Table 1 shows the conversion ratio of all the available technologies used for biomass processing. Table 1: Conversion ratio of all technologies for each biomass Technology Feedstock Output Conversion Reference Fermentation EFB Bioethanol 0.09 t/t (Nurul Adela et al., 2014) OPF 0.32 t/t (Kumneadklang et al., 2015) OPT 0.223 t/t (Eom et al., 2015) Slow Pyrolysis EFB Biochar Biooil Syngas 0.109 t/t; 0.099 t/t; 0.116 t/t (Kong et al., 2014) OPF 0.099 t/t; 0.090 t/t; 0.105 t/t (Kong et al., 2014) OPT 0.079 t/t; 0.072 t/t; 0.084 t/t (Kong et al., 2014) PKS 0.290 t/t; 0.264 t/t; 0.308 t/t (Kong et al., 2014) Fast Pyrolysis EFB Biochar Biooil Syngas 0.040 t/t; 0.248 t/t; 0.043 t/t (Kong et al., 2014) OPF 0.036 t/t; 0.225 t/t; 0.039 t/t (Kong et al., 2014) OPT 0.029 t/t; 0.18 t/t; 0.031 t/t (Kong et al., 2014) PKS 0.106 t/t; 0.660 t/t; 0.114 t/t (Kong et al., 2014) Anaerobic Digestion (AD) EFB Bio- methane 45 m3/t (Suksong et al., 2020) OPF 42 m3/t (Suksong et al., 2020) OPT 35 m3/t (Suksong et al., 2020) POME 10 m3/t (Madaki and Seng, 2013) Gasification EFB Syngas 2,178 m3/t (Sukiran et al., 2011) OPF 2,223 m3/t (Konda et al., 2012) OPT 2,105 m3/t (Nipattummakul et al., 2012) The annual working hour and payout period for the system is 8,000 h/y and 10 y. The annual biomass input is calculated based on 205,882 t/y FFB, which gives a maximum available biomass of 347,735 t/y. Table 2 shows the conversion ratio of FFB (fresh fruit bunch), biomass availability and their cost and emission parameters. Table 3 shows the selling price of biomass products and the capital expenditure (CAPEX) and operational expenditure (OPEX) of biomass processing technologies. Table 2: Conversion ratio of FFB, availability of biomass, cost of biomass, and emission parameter of biomass Biomass Conversion Ratio1 (t output/t of FFB) Available Biomass (t/y) Total Raw Material Cost (MYR/t) Carbon Emission2 [kg CO2 / t biomass transported] PKS 0.069 14,206 250.40 0.519 EFB 0.230 47,353 50.40 0.519 POME 0.599 123,323 0 0 OPT 0.100 142,264 254.17 5.367 OPF 0.691 20,588 54.17 5.367 Source: 1Yeo et al. (2020), 2Wang and Yang (2022) Table 3: Biomass product selling prices and cost of biomass processing technologies Product Unit Price (MYR/unit) Technology CAPEX (MYR/t) OPEX (MYR/t) Bioethanol1 t 2661.6 Fermentation4 159.00 260.00 Biochar1 t 1260 Slow Pyrolysis4 173.00 108.00 Bio-oil2 t 917 Fast Pyrolysis4 141.00 171.00 Syngas2 m3 0.6 Anaerobic Digestion3 261.10 2.69 Bio-methane3 m3 1.13 Gasification4 150.00 180.00 Source: 1MacRelli et al. (2012); 2How (2018); 3How et al. (2018); 4Yeo et al. (2020) The environmental dimension is measured according to the total emission of carbon dioxide from the process and transportation of biomass. Table 4 tabulates the steam and electricity requirement of each technology. 567 Table 5 shows the carbon emission parameters for biomass transportation and processing technology. The emission rate of transportation is 2.77 kg CO2 per L of Diesel (Wang and Yang, 2022). The rate of carbon emission from power generation is 1.18 kg CO2 per kWh of electricity consumed. Pyrolysis process generally offers negative carbon emission (Hammond et al., 2011). Table 4: Scale factors of steam and electricity demand for each technology Technology Steam (t MPS/t biomass) Electricity (kWh/t biomass) Reference Fermentation - 62.46 (Kumar and Murthy, 2011) Slow Pyrolysis - 150 (Humbird et al., 2011) Fast Pyrolysis - 180 (Humbird et al., 2011) Anaerobic Digestion - - - Gasification 0.45 280 (Humbird et al., 2011) Table 5: Carbon emission rate of conversion technology Technology Feedstock Emission rate [t CO2 / t biomass] Technology Feedstock Emission rate [t CO2 / t biomass] Process Power generation Process Power generation Fermentation EFB 0.084 0.074 Anaerobic EFB 0.079 0 OPF 0.300 Digestion OPF 0.074 OPT 0.079 OPT 0.043 Slow Pyrolysis EFB - 0.177 POME 0.036 OPF - Fast Pyrolysis EFB - 0.212 OPT - OPF - PKS - OPT - Gasification EFB - 0.330 PKS - Figure 1: P-graph model of the case study (blue box represents the total carbon emission) The benchmark of total carbon emission allowance is set based on the estimated carbon emission from landfilling palm biomass at a rate of 400 kg CO2eq/ t biomass. The total carbon emission allowance of the biomass conversion process is assumed to be 50 % of the total carbon emission from landfilling process, which takes the value of 69,547 t CO2eq/y. Figure 1 shows the P-graph model of the case study constructed using P- graph Studio. 568 3. Results and discussion 3.1 Optimization using P-graph model The optimisation in P-graph is performed using ABB algorithm. The optimal solution gives a profit of 225,447,000 MYR/y. PKS, EFB, OPT and OPF were utilised to produce 1,619 t/y of biochar, 9,376 t/y of bio-oil, and 462,728,000 m3/y of syngas with POME not being utilised. Table 6 summarises the top 5 solutions generated by the P-graph model. 3.2 Pareto visualisation The solutions derived from P-graph is portrayed using Pareto chart (as shown in Figure 2) which allows the simultaneous display of economic performance and environmental performance for each solution. As the most optimal solution may not be the most ideal solution in practice, the Pareto chart allows the selection of solution(s) according to a company’s emission target. Table 6: Top 5 results generated by P-graph model Rank Profit (MYR/y) Total Carbon Emission (t CO2/y) Pathway Selection 1 226,476,000 73,286.0 PKS β†’ Fast Pyrolysis EFB β†’ Gasification OPT β†’ Gasification OPF β†’ Gasification POME β†’ Unutilised 2 225,641,000 72,788.8 PKS β†’ Slow Pyrolysis EFB β†’ Gasification OPT β†’ Gasification OPF β†’ Gasification POME β†’ Unutilised 3 222,018,000 70,267.0 PKS β†’ Unutilised EFB β†’ Gasification OPT β†’ Gasification OPF β†’ Gasification POME β†’ Unutilised 4 221,027,000 69,641.9 PKS β†’ Fast Pyrolysis EFB β†’ Gasification OPT β†’ Fermentation OPF β†’ Gasification POME β†’ Unutilised 5 210,192,000 69,144.7 PKS β†’ Slow Pyrolysis EFB β†’ Gasification OPT β†’ Fermentation OPF β†’ Gasification POME β†’ Unutilised Typical optimisation objectives are to maximise the profit and minimise the total carbon emission. These two objectives are generally opposed to each other, as total carbon emissions typically increase with profit. The optimal solution is determined by finding the maximum profit achievable within the given carbon emission allowance. If the CO2 emission allowance is set to 50% (69,546.94 t/y) of what would be caused by landfilling biomass, the Rank 1 solution does not meet the specification, but Structure 5 solution does. The results indicate that converting palm biomass into value-added products releases less carbon into the atmosphere compared to landfilling biomass. In short, the model is capable of generating solutions that meet the objectives of this study for both economic and environmental indicators (i.e., a positive gross profit and an acceptable reduction in total carbon emissions). The results prove the feasibility of a palm biomass waste-to-wealth processing network and provide a convincing note regarding economic and environmental performance to the industry and investors. Figure 2: Pareto chart plotted for top 100 solutions in descending profit 4. Conclusions An optimisation model for palm biomass waste-to-wealth processing network was constructed through P-graph approach. The biomass conversion technologies available for PKS, EFB, POME, OPT and OPF were considered to convert biomass into valuable products with their potential carbon emissions evaluated. With the generated solutions using P-graph, Pareto chart was used to screen solution with acceptable total carbon emission allowance benchmarked at 50 % of the carbon emission caused by biomass landfilling. Solution Structure 5 was identified as the feasible solution meeting the emission criteria while generating positive income. 0 20 40 60 80 100 0 50 100 150 200 250 S tr u c tu re 1 S tr u c tu re 3 S tr u c tu re 5 S tr u c tu re 7 S tr u c tu re 9 S tr u c tu re 1 1 S tr u c tu re 1 3 S tr u c tu re 1 5 S tr u c tu re 1 7 S tr u c tu re 1 9 S tr u c tu re 2 1 S tr u c tu re 2 3 S tr u c tu re 2 5 S tr u c tu re 2 7 S tr u c tu re 2 9 S tr u c tu re 3 1 S tr u c tu re 3 3 S tr u c tu re 3 5 S tr u c tu re 3 7 S tr u c tu re 3 9 S tr u c tu re 4 1 S tr u c tu re 4 3 S tr u c tu re 4 5 S tr u c tu re 4 7 S tr u c tu re 4 9 S tr u c tu re 5 1 S tr u c tu re 5 3 S tr u c tu re 5 5 S tr u c tu re 5 7 S tr u c tu re 5 9 S tr u c tu re 6 1 S tr u c tu re 6 3 S tr u c tu re 6 5 S tr u c tu re 6 7 S tr u c tu re 6 9 S tr u c tu re 7 1 S tr u c tu re 7 3 S tr u c tu re 7 5 S tr u c tu re 7 7 S tr u c tu re 7 9 S tr u c tu re 8 1 S tr u c tu re 8 3 S tr u c tu re 8 5 S tr u c tu re 8 7 S tr u c tu re 8 9 S tr u c tu re 9 1 S tr u c tu re 9 3 S tr u c tu re 9 5 S tr u c tu re 9 7 S tr u c tu re 9 9 C O 2 e m is s io n ( k t/ y) P ro fi t (x 1 0 6 M Y R /y ) Solutions Profit (MYR/y) CO2 (t/y) 50% CO2 allowance 569 The feasible outcome of the study provides a convincing statement to the industry and investors to explore further the biomass waste-to-wealth supply network. 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