• Energy Research

Abstract Waste management requires a new vision and drastic improvements for a transition to a zero-waste circular economy. In reality, however, many economies are producing more and more waste, which poses a serious challenge to environmental sustainability. The problem is enormously complex as it involves a variety of stakeholders, demands behavioral changes, and requires a complete rethinking of the current waste management systems and the dominant linear economic model. Smart enabling technologies can aid in a transformation of waste management toward a circular economy, but many barriers persist. This study first shortlists twelve important barriers to smart waste management in China based on interviews with experienced practitioners. It then prioritizes these barriers through a scientific prioritization technique, fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), based on the survey data from three representative stakeholders. It identified three key causal barriers: the lack of regulatory pressures, the lack of environmental education and culture of environmental protection, and the lack of market pressures and demands. Practical and theoretical implications were discussed based on the research results and findings.

Abstract Production scheduling has great significance for optimizing tasks distribution, reducing energy consumption and mitigating environmental degradation. Currently, the research of production scheduling considering energy consumption mainly focuses on the traditional manufacturing workshop. With the wide application of the Internet of Things (IoT) technology, the real-time data of manufacturing resources and production processes can be retrieved easily. These manufacturing data can provide opportunities for manufacturing enterprises to reduce energy consumption and enhance production efficiency. To achieve these targets, a multi-period production planning based real-time scheduling (MPPRS) approach for the IoT-enabled low-carbon flexible job shop (LFJS) is presented in this study to carry out real-time scheduling based on the real-time manufacturing data. Then, the mathematical models of real-time scheduling are established to achieve production efficiency improvement and energy consumption reduction. To obtain a feasible solution, an infinitely repeated game optimization approach is used. Finally, a case study is implemented to analyse and discuss the effectiveness of the proposed method. The results show that in general, the proposed method can achieve better results than the existing dynamic scheduling methods.

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact. Funding Agencies|VinnovaVinnova [2017-01649]

AbstractE-waste is one of the fastest growing streams of solid waste globally, and its effective management has become a focused issue, which requires a deep understanding of the core guiding theory of extended producer responsibility (EPR). Over the past 20 years, China, one of the world’s largest producers of electrical and electronic equipment (EEE), has made great efforts to improve e-waste management along with the massive generation of e-waste. In 2012, China implemented a unique EPR-based e-waste fund policy. However, the fund policy is unsustainable due to the challenges of non-closed resource use, informal recycling, and fund imbalance. Beginning with an overview of these challenges, this paper focuses on redesigning the fund policy from a closed-loop lifecycle perspective in order to maintain a balanced development of the resource use loop and the fund system in China’s ten-year plan. In doing so, two EPR instruments, recycling content standards and consumer-oriented deposits, are added to the current fund policy. Subsequently, three extension scenarios alternately changed a critical parameter of the model to test the impact on sustainable capabilities. In this way, the sustainable supply of funds and secondary resources for the e-waste industry can be established in China and effectively demonstrate solid waste management in developing countries.

More than 110 countries, including 500 cities worldwide, have set the goal of reaching carbon neutrality. Heating contributes to most of the residential energy consumption and carbon emissions. The green energy transition of fossil-based heating systems is needed to reach the emission goals. However, heating systems vary in energy source, heating technology, equipment location, and these complexities make it challenging for households to compare heating systems and make decisions. Hence, a decision support tool that provides a generalized ranking of individual heating alternatives is proposed for households as decision makers to identify the optimal choice. This paper presents an analysis of 13 heating alternatives and 19 quantitative criteria in technological, environmental, and financial aspects, combines ideal solution-based multi-criteria decision making with 6 weighting methods and 4 normalization methods, and introduces ensemble learning with a fuzzy membership function derived from Cauchy distribution to finalize the ultimate ranking. The robustness of the proposed method is verified by three sensitive analyses from different aspects. Air-to-water heat pump, solar heating and direct district heating are the top three rankings in the final result under Danish national average data. A framework is designed to guide decision makers to apply this ranking guideline with their practical, feasible situations.

Abstract Energy-intensive industries account for almost 51% of energy consumption in China. A continuous improvement in energy efficiency is important for energy-intensive industries. Cleaner production has proven itself as an effective way to improve energy efficiency and reduce energy consumption. However, there is a lack of manufacturing data due to the difficult implementation of sensors in harsh production environment, such as high temperature, high pressure, high acid, high alkali, and smoky environment which hinders the implementation of the cleaner production strategy. Thanks to the rapid development of the Internet of Things, many data can be sensed and collected in the manufacturing processes. In this paper, a big data driven analytical framework is proposed to reduce the energy consumption and emission for energy-intensive manufacturing industries. Then, two key technologies of the proposed framework, namely energy big data acquisition and energy big data mining, are utilized to implement energy big data analytics. Finally, an application scenario of ball mills in a pulp workshop of a partner company is presented to demonstrate the proposed framework. The results show that the energy consumption and energy costs are reduced by 3% and 4% respectively. These improvements can promote the implementation of cleaner production strategy and contribute to the sustainable development of energy-intensive manufacturing industries.

It is critical to arrange appropriate charging infrastructure in advance to decarbonise heavy freight through electrification. Based on the same service level, this study conducted a techno-economic comparison of charging modes for battery heavy-duty vehicles in short-haul delivery, covering the broadest range of charging modes, including slow, fast (150 kW), fast (350 kW), swap, and overhead catenary. The techno-economic performance is obtained by a model composed of five evaluation indicators, in which the ratio of service capacity to cost and the average financial net present value are specific. The factors influencing the techno-economic performance of charging modes are further explored based on case analysis. Results show that the charging modes of slow, fast (150 kW), fast (350 kW), swap, and overhead catenary are not profitable under the corresponding facility utilisation rates of 40%, 20%, 20%, 30%, and 70%, or under operating years of 5, 3, 2, 4, and 12 years. Fast charging, at both 150 and 350 kW, has a better advantage in profitability based on the highest average financial net present value. Swap charging is best regarding energy supplement efficiency, but it is not profitable when the battery swapping price is less than 0.8 CNY/kWh. Overhead catenary charging is the most effective system per unit cost due to the highest ratio of service capacity to cost. The insights, the precise prediction of the charging demand, the focus on the charging price, and the comprehensive improvement in the facility utilisation rate are crucial for the success of charging service providers. Funding Agencies|Innovative Research Team Development Program of the Edu-cation Ministry in China [IRT_17R83]