Existing mitigation strategies to reduce greenhouse gas (GHG) emissions are inadequate to reach the target emission reductions set in the Paris Agreement. Hence, the deployment of negative emission technologies (NETs) is imperative. Given that there are multiple available NETs that need to be evaluated based on multiple criteria, there is a need for a systematic method for ranking and prioritizing them. Furthermore, the uncertainty in estimating the techno-economic performance levels of NETs is a major challenge. In this work, an integrated model of fuzzy analytical hierarchy process (AHP) and interval-extended Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed to address the multiple criteria, together with data uncertainties. The potential of NETs is assessed through the application of this hybrid decision model. Sensitivity analysis is also conducted to evaluate the robustness of the ranking generated. The result shows Bioenergy with Carbon Capture and Storage (BECCS) as the most optimal alternative for achieving negative emission goals since it performed robustly in the different criteria considered. Meanwhile, energy requirement emerged as the most preferred or critical criterion in the deployment of NETs based on the decision-maker. This paper renders a new research perspective for evaluating the viability of NETs and extends the domains of the fuzzy AHP and interval-extended TOPSIS hybrid model.
Identification of appropriate clean technologies for industrial implementation requires systematic evaluation based on a set of criteria that normally reflect economic, technical, environmental and other aspects. Such multiple attribute decision-making (MADM) problems involve rating a finite set of alternatives with respect to multiple potentially conflicting criteria. Conventional MADM approaches often involve explicit trade-offs in between criteria based on the expert's or decision maker's priorities. In practice, many experts arrive at decisions based on their tacit knowledge. This paper presents a new induction approach, wherein the implicit preference rules that estimate the expert's thinking pathways can be induced. P-graph framework is applied to the induction approach as it adds the advantage of being able to determine both optimal and near-optimal solutions that best approximate the decision structure of an expert. The method elicits the knowledge of experts from their ranking of a small set of sample alternatives. Then, the information is processed to induce implicit rules which are subsequently used to rank new alternatives. Hence, the expert's preferences are approximated by the new rankings. The proposed induction approach is demonstrated in the case study on the ranking of Negative Emission Technologies (NETs) viability for industry implementation.
This article contains the data set and model code for the negative emission polygeneration system described in Tan et al. (2019). The data was generated utilizing an optimization model implemented in LINGO 18.0 and includes information on the operating state of each process unit in the system. The maximum annual profit of the system was determined at different carbon footprint targets. The data set and model code can be utilized for further analysis on the interdependence between the process units of this polygeneration system, its operational and environmental performance, and the potential impact of integrating new process units into the network.
Plastic waste and its environmental hazards have been attracting public attention as a global sustainability issue. This study builds a neural network model to forecast plastic waste generation of the EU-27 in 2030 and evaluates how the interventions could mitigate the adverse impact of plastic waste on the environment. The black-box model is interpreted using SHapley Additive exPlanations (SHAP) for managerial insights. The dependence on predictors (i.e., energy consumption, circular material use rate, economic complexity index, population, and real gross domestic product) and their interactions are discussed. The projected plastic waste generation of the EU-27 is estimated to reach 17 Mt/y in 2030. With an EU targeted recycling rate (55%) in 2030, the environmental impacts would still be higher than in 2018, especially global warming potential and plastic marine pollution. This result highlights the importance of plastic waste reduction, especially for the clustering algorithm-based grouped countries with a high amount of untreated plastic waste per capita. Compared to the other assessed scenarios, Scenario 4 with waste reduction (50% recycling, 47.6% energy recovery, 2.4% landfill) shows the lowest impact in acidification, eutrophication, marine aquatic toxicity, plastic marine pollution, and abiotic depletion. However, the global warming potential (8.78 Gt CO2eq) is higher than that in 2018, while Scenario 3 (55% recycling, 42.6% energy recovery, 2.4% landfill) is better in this aspect than Scenario 4. This comprehensive analysis provides pertinent insights into policy interventions towards environmental hazard mitigation.
Biochar is a high-carbon-content organic compound that has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant-based, animal-based, and municipal waste at different pyrolysis conditions. However, it is difficult to produce biochar on a large scale if the relationship between the type of biomass, operating conditions, and biochar properties is not understood well. Hence, the use of machine learning-based data analysis is necessary to find the relationship between biochar production parameters and feedstock properties with biochar energy properties. In this work, a rough set-based machine learning (RSML) approach has been applied to generate decision rules and classify biochar properties. The conditional attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, and oxygen) and pyrolysis conditions (operating temperature, heating rate residence time), while the decision attributes considered were yield, carbon content, and higher heating values. The rules generated were tested against a set of validation data and evaluated for their scientific coherency. Based on the decision rules generated, biomass with ash content of 11-14 wt%, volatile matter of 60-62 wt% and carbon content of 42-45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via a pyrolysis process at an operating temperature of 425°C-475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield.