Rethinking of the future sustainable paradigm roadmap for plastic waste management: A multi-nation scale outlook compendium

The myriad consumption of plastic regularly, environmental impact and health disquietude of humans are at high risk. Along the line, international cooperation on a global scale is epitomized to mitigate the environmental threats from plastic usage, not limited to implementing international cooperation strategies and policies. Here, this study aims to provide explicit insight into possible cooperation strategies between countries on the post-treatment and management of plastic. First, a thorough cradle-to-grave assessment in terms of economic, environmental, and energy requirements is conducted on the entire life cycle across different types of plastic polymers in 6 main countries, namely the United States of America, China, Germany, Japan, South Korea, and Malaysia. Subsequently, P-graph is introduced to identify the integrative plastic waste treatment scheme that minimizes the economic, environmental, and energy criteria (1000 sets of solutions are found). Furthermore, TOPSIS analysis is also being adapted to search for a propitious solution with optimal balance between the dominant configuration of economic, environmental, and energy nexus. The most sustainable configuration (i.e., integrated downcycle and reuse routes in a closed loop system except in South Korea, which proposed another alternative to treat the plastic waste using landfill given the cheaper cost) is reported with 4.08 × 108 USD/yr, 1.76× 108 kg CO2/yr, and 2.73 × 109 MJ/yr respectively. To attain a high precision result, Monte-Carlo simulation is introduced (10,000 attempts) to search for possible uncertainties, and lastly, a potential global plastic waste management scheme is proposed via the PESTLE approach

Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses

The catalytic pyrolysis of Chlorella vulgaris, high-density polyethylene (Pure HDPE) and, their binary mixtures were conducted to analyse the kinetic and thermodynamic performances from 10 to 100 K/min. The kinetic parameters were computed by substituting the experimental and ANN predicted data into these iso-conversional equations and plotting linear plots. Among all the iso-conversional models, Flynn-Wall-Ozawa (FWO) model gave the best prediction for kinetic parameters with the lowest deviation error (2.28–12.76%). The bifunctional HZSM-5/LS catalysts were found out to be the best catalysts among HZSM-5 zeolite, natural limestone (LS), and bifunctional HZSM-5/LS catalyst in co-pyrolysis of binary mixture of Chlorella vulgaris and HDPE, in which the Ea of the whole system was reduced from range 144.93–225.84 kJ/mol (without catalysts) to 75.37–76.90 kJ/mol. With the aid of artificial neuron network and genetic algorithm, an empirical model with a mean absolute percentage error (MAPE) of 51.59% was developed for tri-solid state degradation system. The developed empirical model is comparable to the thermogravimetry analysis (TGA) experimental values alongside the other empirical model proposed in literatur

Optimal planning of inter-plant hydrogen integration (IPHI) in eco-industrial park with P-graph and game theory analyses

With the rising demand for hydrogen in petrochemical and refineries complexes, the optimisation of hydrogen utility is getting more attention. Through inter-plant hydrogen integration (IPHI), the overall hydrogen consumption and purged gases could be further reduced by exchanging hydrogen gases among multiple plants which could reduce the climate change effect. In this work, a P-graph methodology is proposed for the optimal design of IPHI with regeneration-reuse/recycle via a centralised utility hub. Green hydrogen is incorporated in this work in the call for climate change adaption. A case study involving green hydrogen sourced from solar energy, palm oil mill effluent, and wastewater was used to demonstrate the proposed methodology. Four integration schemes were analysed using game theory-based approach for decision making. In IPHI, each participating plant may seek to maximise its own benefits due to rational self-interest. Hence, a game theory-based approach was used to analyse the interaction of participating plants in developing the IPHI schemes. With the implementation of carbon tax, it is potential for motivating collaborations as additional gains can be achieved through collaboration compared to short-sighted self-interest decision. The proposed methodology indicates that collective welfare can be maximised through cooperation among all networks to pursue Pareto optimality and in line with the commitment to tackle climate change and reaching sustainainability agenda

Frontier of digitalization in Biomass-to-X supply chain: opportunity or threats?

The escalating climate crisis necessitates an urgent shift towards a sustainable business model. Under the context of bioeconomy, it has offered a promising alternative through its “Biomass-to-X” strategy for converting biological resources into value-added products or chemicals. However, the adoption of this approach remains scarce, which highlights the need to leverage digital technologies to enhance its feasibility. Thus, this paper provides a comprehensive overview of the potential role of digital technologies in the Biomass-to-X supply chain, encompassing the entire value chain from upstream to downstream activities, specifically in the areas of 1) lab-to-fabrication translation, 2) biomanufacturing stage, and lastly, 3) supply chain management stage. Furthermore, this study identifies and discusses research gaps in each niche area, along with potential future research prospects to facilitate the transition towards a sustainable bioeconomy, making it a crucial reference for stakeholders involved in decision-making processes

Active Learning-Based Guided Synthesis of Engineered Biochar for CO₂ Capture

Biomass waste-derived engineered biochar for CO₂ capture presents a viable route for climate change mitigation and sustainable waste management. However, optimally synthesizing them for enhanced performance is time- and labor-intensive. To address these issues, we devise an active learning strategy to guide and expedite their synthesis with improved CO₂ adsorption capacities. Our framework learns from experimental data and recommends optimal synthesis parameters, aiming to maximize the narrow micropore volume of engineered biochar, which exhibits a linear correlation with its CO₂ adsorption capacity. We experimentally validate the active learning predictions, and these data are iteratively leveraged for subsequent model training and revalidation, thereby establishing a closed loop. Over three active learning cycles, we synthesized 16 property-specific engineered biochar samples such that the CO₂ uptake nearly doubled by the final round. We demonstrate a data-driven workflow to accelerate the development of high-performance engineered biochar with enhanced CO₂ uptake and broader applications as a functional material.ISSN:0013-936XISSN:1520-585

Machine learning–assisted CO2 utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses

The catalytic dry reforming (DR) process is a clean approach to transform CO2 into H2 and CO-rich synthetic gas that can be used for various energy applications such as Fischer–Tropsch fuels production. A novel framework is proposed to determine the optimum reaction configurations and reaction pathways for DR of C1-C4 hydrocarbons via a reaction mechanism generator (RMG). With the aid of machine learning, the variation of thermodynamic and microkinetic parameters based on different reaction temperatures, pressures, CH4/CO2 ratios and catalytic surface, Pt(111), and Ni(111), were successfully elucidated. As a result, a promising multicriteria decision-making process, TOPSIS, was employed to identify the optimum reaction configuration with the trade-off between H2 yield and CO2 reduction. Notably, the optimum conditions for the DR of C1 and C2 hydrocarbons were 800°C at 3 atm on Pt(111); whereas C3 and C4 hydrocarbons found favor at 800°C and 2 atm on Ni(111) to attain the highest H2 yield and CO2 conversion. Based on the RMG-Cat (first-principle microkinetic database), the energy profile of the most selective reaction pathway network for the DR of CH4 on Pt(111) at 3 atm and 800°C was deducted. The activation energy (Ea) for CH bond dissociation via dehydrogenation on the Pt(111) was found to be 0.60 eV, lower than that reported previously for Ni(111), Cu(111), and Co(111) surfaces. The most endothermic reaction of the CH4 reforming process was found to be C3H3* + H2O* ↔ OH* + C3H4 (218.74 kJ/mol)