Data-driven Optimization of Biomass Retrofitting Pathway to Empower Circularity for the Oil and Gas Transition

Abstract

The global effort to hasten the energy transition toward renewable energy challenged the major oil and gas (O&G) industry toward greener revolutionary changes. However, the complexity of the situation made the decision-makers contemplates to achieve a balance between economic and environmental sustainability. Circular economy (CE) is viewed as a potential alternative to pave a greener path for the O&G industry. In this work, retrofitting the O&G industry with biomass conversion technology is proposed to strategize toward a circular industry with the deployment of data-driven optimization approach. Multiple systematic analytical tools (e.g., multi-objective decision analysis (MODM), information entropy) are utilized to synthesize an optimal integration strategy. The developed model determines the optimal biomass retrofitting pathway by evaluating the performances (i.e., revenue, energy consumption, operating expenditure (OPEX), capital expenditure (CAPEX), carbon emissions) of various biomass conversion technologies. Based on the accumulated biomass technology data, the model assigns higher priority to CAPEX, (wj = 0.2764), which has higher sensitivity to change compared to the other criteria (wj = 0.2134 for carbon emissions, wj = 0.2073 for energy consumption, wj = 0.1824 for OPEX, wj = 0.1206 for revenue). The developed model recommends pyrolysis as the optimal pathway given its greatest overall performance score of 91.44 % from TOPSIS. This paper demonstrates the use of data-driven optimization adapting Shannon entropy and TOPSIS to determine the optimal biomass retrofitting pathway for O&G industry towards circularity.