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Optimisation of biochar-based supply chains for negative emissions and resource savings in carbon management networks

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Abstract

Climate change is a global issue, and the main reason causing climate change is carbon dioxide (CO2) emissions. Biochar, a stable form of carbon that can be obtained from pyrolysis of biomass, can potentially reduce CO2 emissions. By applying biochar into soil, a stable form of carbon is stored under the soil. Biochar application is able to achieve negative emissions because the stored carbon was previously fixed from CO2 via photosynthesis. Biochar application also brings additional benefits to soil as it helps to retain water and nutrients in soil. The benefits of biochar can be scaled up by developing biochar-based carbon management networks (CMNs). However, there are very limited decision support tools available for designing optimal biochar-based CMNs. This work presents a methodology to develop a decision support tool for biochar-based CMNs. Proposed methodology provides guidance on how a mixed-integer linear mathematical model can be formulated as a decision support tool to design biochar-based CMNs. The developed model has capabilities to determine the optimal network based on cost while considering several constraints. These constraints are CO2 emission reduction, water and nutrient retention and several land constraints. The network costs considered are capital cost, operating cost, transportation cost and transport fuel consumption cost. The model also factors transport routes, hiring cost and truck capacity into the network. A case study based on the palm oil industry is solved to demonstrate the use of the model. Results from the case study show that total biochar production, water savings, nutrient savings, cost savings and total network costs were 307 t/day, 7.1 M m3 water/year, 6.0 k t nutrients/year, RM 25.6 M/year and RM 17.9 M/year, respectively. Besides that, a sensitivity analysis is done to analyse the impact of CO2 emission reduction targets on the network costs. Aside from this, two alternative scenarios from the case study were investigated further.

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Abbreviations

I :

Index for biochar source

r :

Index for biomass

j :

Index for pyrolysis

p :

Index for product

k :

Index for logistics

q :

Index for biochar sink

\(\varphi_{rjp}\) :

Conversion factor from biomass r to product p through pyrolysis j

\({\text{F}}_{ij}^{\text{Min}}\) :

Lower limit of biochar production in t/day at biochar source i by pyrolysis

\({\text{F}}_{ij}^{\text{Max}}\) :

Upper limit of biochar production in t/day at biochar source i by pyrolysis

\({\text{CT}}_{k}\) :

Truck capacity in t of logistic k

\({\text{F}}_{ipq}^{\text{Max}}\) :

Maximum flowrate of biochar produced p from source to sink in t/day

\({\text{R}}_{pq}^{\text{CO2}}\) :

CO2 reduction by biochar q applied in sink q in t CO2/t biochar

\({\text{R}}_{{_{pq} }}^{\text{NT}}\) :

Nutrient retention by biochar q applied in sink q in t nutrient/t biochar

\({\text{R}}_{{_{pq} }}^{\text{WT}}\) :

Water retention by biochar q applied in sink q in m3 water/t biochar

\({\text{SR}}_{pq}^{{}}\) :

Soil priming effect of biochar p applied in sink q

\({\text{NT}}_{q}^{{\text{Re} q}}\) :

Nutrient requirement at sink q in t nutrient

\({\text{WT}}_{q}^{{\text{Re} q}}\) :

Water requirement at sink q in m3 water

\({\text{FC}}^{\text{NT}}\) :

Cost of nutrient in RM/t nutrient

\({\text{FC}}^{\text{WT}}\) :

Cost of water in RM/m3 water

\({\text{FC}}_{ij}^{\text{CAPEX}}\) :

Fixed CAPEX for pyrolysis j for biochar sink I in RM

\({\text{VC}}_{ij}^{\text{CAPEX}}\) :

Variable CAPEX for pyrolysis j for biochar sink I in RM

\({\text{AF}}\) :

Annualised cost factor for a period of 10 years

\({\text{FC}}_{ij}^{\text{OpC}}\) :

Fixed operating cost for pyrolysis j for biochar sink I in RM/year

\({\text{VC}}_{ij}^{\text{OpC}}\) :

Variable operating cost for pyrolysis j for biochar sink I in RM/year

\({\text{D}}_{iq}\) :

Distance from biochar source i to sink q in km

\({\text{HC}}_{k}^{\text{Trans}}\) :

Hiring cost of transportation for logistics k

\({\text{M}}_{k}^{\text{Fuel}}\) :

Fuel consumption of logistic k in L/t km

\({\text{FC}}_{{}}^{\text{Fuel}}\) :

Fuel cost in RM/L

\({\text{VC}}_{k}^{\text{Trans}}\) :

Variable cost of transportation for logistics k

\(F_{ri}^{\text{Av}}\) :

Flowrate of biomass r available at biochar source i in t/day

\(F_{irj}\) :

Flowrate of biomass r to pyrolysis j at biochar source i in t/day

\(F_{ijp}\) :

Flowrate of biomass r produced by pyrolysis j to product p in t/day

\(I_{ij}\) :

ON/OFF state of pyrolysis j at biochar source i

\(I_{iqk}\) :

ON/OFF state of logistic to transport biochar from biochar source i to sink q

\(N_{iqk}\) :

Number of trips of logistic k to transport biochar from biochar source i to sink q

\(F_{{_{pq} }}^{{\text{Re} v}}\) :

Flowrate of biochar product p received at sink q in t/day

\(R^{\text{CO2}}\) :

Total CO2 reduction of network in t CO2/year

\(NT_{{_{q} }}\) :

Total nutrient retention of network in t nutrient/year

\(WT_{{_{q} }}\) :

Total water retention of network in m3 water/year

\(CR^{\text{NT}}\) :

Cost savings for nutrients in RM/year

\(CR^{\text{WT}}\) :

Cost savings for water in RM/year

\(C^{\text{CAPEX}}\) :

Annualised CAPEX of network in RM/year

\(C^{\text{Trans}}\) :

Transporting cost of the network in RM/year

\(C^{\text{Fuel}}\) :

Fuel consumption cost of the network in RM/year

\(C^{\text{Total Trans}}\) :

Total transportation cost of the network in RM/year

\(C^{\text{OPEX}}\) :

Operating cost of the network in RM/year

\(CR^{\text{TOTAL}}\) :

Total cost saving of the network in RM/year

\(C^{\text{Net}}\) :

Network cost in RM/year

CMN:

Carbon management network

GHG:

Greenhouse gas

MILP:

Mixed-integer linear programming

POM:

Palm oil mill

PT:

Plantation

EFB:

Empty fruit bunch

OPEX:

Operating expenditure

CAPEX:

Capital expenditure

SPY:

Slow pyrolysis

FPY:

Fast pyrolysis

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Acknowledgements

Authors acknowledge the support from the School of Engineering and Physical Sciences (EPS) at Heriot-Watt University Malaysia. Authors also acknowledge LINDO SYSTEMS INC. for providing academic licence to conduct this research.

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Authors and Affiliations

  1. School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, 1, Jalan Venna P5/2, Precinct 5, 62200, Putrajaya, Wilayah Persekutuan, Malaysia

    Shi Hui Ong & Viknesh Andiappan

  2. Chemical Engineering Department, De La Salle University, 2401 Taft Avenue, 0922, Manila, Philippines

    Raymond R. Tan

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  1. Shi Hui Ong
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Correspondence to Viknesh Andiappan.

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Ong, S.H., Tan, R.R. & Andiappan, V. Optimisation of biochar-based supply chains for negative emissions and resource savings in carbon management networks. Clean Techn Environ Policy 23, 621–638 (2021). https://doi.org/10.1007/s10098-020-01990-0

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