Navigating the Methanol Paradox: A Prescriptive LCA-DEAFramework for Benchmarking Diverse Production Pathways
Natthawut Praneetpolgrang, Narawit Yingyong, Kittipon Jaraswimol, Chayet Worathitanon, Viganda Varabuntoonvit, Phantisa Limleamthong
Figshare · 2026
Methanol is a pivotal carrier for the emerging hydrogen economy, yet the environmental trade-offs between conventional fossil-based and emerging renewable production pathways remain complex. This study introduces an integrated benchmarking framework coupling comprehensive process modeling, Life Cycle Assessment (LCA), and Super-Efficiency Data Envelopment Analysis (DEA) to evaluate 11 methanol production pathways. Results identify five renewable-integrated benchmarks, led by hydropower-driven CO<sub>2</sub> hydrogenation, which achieves top performance by bypassing syngas production and eliminating fossil-fuel dependency. Conversely, pathways relying on the fossil-intensive grid remain inefficient due to the high toxicological and carbon intensity of compression and O<sub>2</sub> production. Beyond identifying bottlenecks, the DEA framework endogenously delineates technological mentors for suboptimal pathways based on structural kinship and feedstock homology, ensuring that prescribed retrofit trajectories are practically feasible. While CO<sub>2</sub> hydrogenation maximizes theoretical efficiency, reforming-based routes can achieve significant gains by optimizing stoichiometry via CO<sub>2</sub>/O<sub>2</sub> integration. However, a critical Energy-Water-Land nexus trade-off is uncovered: renewable integration effectively neutralizes toxicity but triggers burden shifting toward increased land and water consumption. Furthermore, the carbon capture stage remains a net-positive GWP contributor unless decoupled from fossil-based thermal utilities. By mapping these structural bottlenecks and feasible benchmarks, this work establishes a rigorous mathematical basis for strategic retrofitting, charting a clear technological roadmap toward a circular carbon economy.
Navigating the Methanol Paradox: A Prescriptive LCA-DEA Framework for Benchmarking Diverse Production Pathways
Natthawut Praneetpolgrang, Narawit Yingyong, Kittipon Jaraswimol, Chayet Worathitanon, Viganda Varabuntoonvit, Phantisa Limleamthong
Industrial & Engineering Chemistry Research · 2026
High Resolution Image Download MS PowerPoint Slide Methanol is a pivotal carrier for the emerging hydrogen economy, yet the environmental trade-offs between conventional fossil-based and emerging renewable production pathways remain complex. This study introduces an integrated benchmarking framework coupling comprehensive process modeling, Life Cycle Assessment (LCA), and Super-Efficiency Data Envelopment Analysis (DEA) to evaluate 11 methanol production pathways. Results identify five renewable-integrated benchmarks, led by hydropower-driven CO 2 hydrogenation, which achieves top performance by bypassing syngas production and eliminating fossil-fuel dependency. Conversely, pathways relying on the fossil-intensive grid remain inefficient due to the high toxicological and carbon intensity of compression and O 2 production. Beyond identifying bottlenecks, the DEA framework endogenously delineates technological mentors for suboptimal pathways based on structural kinship and feedstock homology, ensuring that prescribed retrofit trajectories are practically feasible. While CO 2 hydrogenation maximizes theoretical efficiency, reforming-based routes can achieve significant gains by optimizing stoichiometry via CO 2 /O 2 integration. However, a critical Energy-Water-Land nexus trade-off is uncovered: renewable integration effectively neutralizes toxicity but triggers burden shifting toward increased land and water consumption. Furthermore, the carbon capture stage remains a net-positive GWP contributor unless decoupled from fossil-based thermal utilities. By mapping these structural bottlenecks and feasible benchmarks, this work establishes a rigorous mathematical basis for strategic retrofitting, charting a clear technological roadmap toward a circular carbon economy.
Life Cycle Assessment of CO2-to-Methanol: Comparative Evaluation of Direct and Alcohol-Assisted Hydrogenation Routes
Chayet Worathitanon, Naphat Chansonthi, Pawat Pinthong, Kritsana Suwanamad, Viganda Varabuntoonvit
DOAJ (DOAJ: Directory of Open Access Journals) · 2025
The increasing severity of global warming, primarily driven by greenhouse gas emissions, underscores the urgent need for CO2 reduction and utilization strategies. Converting CO2 into methanol presents a promising approach, as methanol serves both as a fuel and a feedstock in various industries. This study evaluates the life cycle environmental impacts of three methanol production routes: (1) direct CO2 hydrogenation, (2) ethanol-assisted CO2 hydrogenation, and (3) propanol-assisted CO2 hydrogenation. Two energy scenarios are considered: conventional energy and wind power. Process simulations were performed using Aspen Plus V.14, and inventories were analyzed through Life Cycle Assessment (LCA) using the ReCiPe 2016 (H) method under a cradle-to-gate approach for 1,000 kg of methanol. The alcohol-assisted processes operated at a lower reaction temperature (150 °C) and consumed less CO2, H2, and compression energy than the conventional process (250 °C), thereby reducing environmental impacts. However, methanol purification remained energy-intensive. Under conventional energy, the propanol-assisted process exhibited the highest global warming potential (GWP), followed by the ethanol-assisted process, while the conventional route showed the lowest. The elevated impact of the propanol route was primarily attributed to higher alcohol feedstock and energy consumption. The use of wind power significantly reduced GWP in the separation stage of alcohol-assisted routes, resulting in the lowest GWP for the ethanol-assisted process.
Enhancing industrial sustainability in complex production systems through energy hotspot identification: A multi-task learning with layer-wise relevance propagation approach
Santi Bardeeniz, Chanin Panjapornpon, M.A. Hussain, Viganda Varabuntoonvit, Kulpavee Jitapunkul
Results in Engineering · 2024
With growing concerns about global warming and environmental degradation, the petrochemical industry has been urged to reduce energy consumption and emissions. However, making operational adjustments in multi-sectional production systems, which involve multiple production stages, is challenging without identifying the areas within the process that are intensive energy consumers. Therefore, this paper proposes a novel hotspot identification methodology in a multi-sectional production system using layer-wise relevance propagation of a multi-task learning model. The proposed model tracks overall energy efficiency as the final output to determine the energy gap, while the activation result identifies areas of production where energy is ineffectively consumed using sectional energy efficiency. The performance of the proposed method is validated using a case study of the vinyl chloride monomer process, which can be divided into five sections. The results show that the proposed methodology effectively captures the relationship between process variables and both tasks (overall and sectional energy efficiency) with an average R 2 of 0.9818 and 0.9858 in prediction with unseen data, respectively. The layer-wise relevance propagation result demonstrates the contribution of unit operations to overall energy efficiency values by tracing the regression trajectories. Furthermore, operational adjustment using the overall energy gap as a benchmark condition offers a significant energy consumption reduction of around 15.87 %, reduces operating expenses for utilities by 147,000 USD, and decreases direct carbon dioxide emissions by 4700 tons, helping production to become more energy-efficient and sustainable. • Real-time industrial hotspot analysis of a multi-sectional system is proposed. • Capturing overall SEC while constrained by sectional SEC using multi-task learning. • Layer-wise relevance propagation tracks prediction gaps to trace energy wastage. • MTL-LRP activation analysis accurately identifies process hotspot locations. • The model uncovers 15 % of energy-saving and reduces 4700 tons of GHG emissions.
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