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Natural gas has been widely promoted by Chinese government as a clean energy source due to its odorless and smokeless combustion process. As an important part of urban natural gas supply, coal bed methane (CBM) accounted for 3.4% of the China's total natural gas production in 2017 and had an increase of 9.2% year-on-year compared with last year. In response to government promotion and so as to maintain the steady production of CBM, some enhancing-production approaches have been taken in CBM fields, and one of the effective approaches is the installation of compressors in gathering networks. However, due to the complexity of CBM gathering networks, the pressurization scheme lacks guidance in practice and is mainly decided by experience. To guide the practical production process, this paper proposes a mixed-integer nonlinear programming (MINLP) model to optimize the pressurization scheme. The MINLP model considers the cost of facilities and electricity and maximizes the daily net profit of CBM field companies. Then, a two-stage improved genetic algorithm-particle swarm optimization algorithm is proposed to solve this problem. Finally, the model is applied to a real CBM field with 132 production wells, 10 manifolds and 1 central processing facility (CPF) in China. The result shows that the optimal scheme can increase the total production from 3.39 × 105 m3/d to 3.73 × 105 m3/d with a growth rate of 10.0%, and the daily net profit increases 2.49 × 104CNY/d (about 3.70 × 103USD/d). • An MINLP model for optimizing the pressurization scheme in CBM fields is established. • A tailored two-stage iGA-PSO algorithm for solving the MINLP model is proposed. • A real CBM field in China is studied to validate the effectiveness of the model and algorithm. [ABSTRACT FROM AUTHOR]

In China, more than 14 million tons of edible oils are consumed each year, which could result in a staggering amount of waste cooking oil that needs to be discharged. Converting the waste cooking oil to biodiesel can reduce environmental emissions and achieve economic value. As the recycling supply chain is not matured, the Chinese government is trialing various policies to improve the management of waste cooking oil. However, evidence shows that current policies have shortcomings, which reduce the efficiency and sustainability of the supply chain, recycling and biorefineries capacities. It is not trivial to quantify the impacts of policies and only limited amount of research has reported the supply and value chain of waste cooking oil to be used as biodiesel. To bridge this gap, this paper proposes an optimization-based model to quantify the likely impacts from policy interventions, i.e. integrating and opening the third party take-back supply chains, motivation policies, subsidy policies and demand adjustments due to policy changes. A case study, which includes 63 stakeholders in Shandong Province, China, is selected to demonstrate the impacts such changes can have. The new research method and computational results of the economic and environmental assessments show that the integration of stakeholders and markets increase efficiency and sustainability. Whilst the opening markets option does make fairer profit allocations across the stakeholders, but does not increase efficencies to the same level. In addition, and to the best of authors' knowledge for the first time, it is shown that unfair profit allocations will decrease the amount of recycled materials. This research has wider applications to policy developments and governmental interventions that aim to promote cleaner production of goods and services. For example, the optimization model can be applied to other circumstances such as electricity supplies, raw material extraction or the recycling of other materials like construction and demolition wastes or plastics. Image 1 • A policy evaluation approach is proposed for the WCO-to-biodiesel supply chain. • Cooperative game theory is considered in the optimization model. • Integrating and opening markets and management modes are compared. • Four different policy interventions are evaluated. • A real case in Shandong province is studied to validate the model. [ABSTRACT FROM AUTHOR]

• An MILP model for DOSC planning is developed. • Distribution plan and new pipeline route plan are optimized simultaneously. • Refined oil transportation from SDs to SDs is considered. • A real-world application of DOSC planning in China is studied. The planning of downstream oil supply chains (DOSCs) is important to guarantee that the demand of retail markets (RMs) can be satisfied. As an economic method for refined oil transport, pipelines are widely used in many DOSCs. The distribution cost can be reduced, and the efficiency can be improved by constructing new pipelines in a DOSC. In this paper, an MILP model that optimises a DOSC with new pipeline route planning is developed. Constraints for refined oil distribution and pipeline route planning are proposed. The model also considers the transport between storage depots (SDs) and makes it practical for the real-world application of a DOSC in China. Based on the current DOSC, the routes of new pipelines and supply chain planning after new pipeline construction can be obtained. This method is applied to a real DOSC case in China, and two situations are analysed. This method can also help decision makers conduct optimal pipeline route planning and create distribution plans for other DOSCs. [ABSTRACT FROM AUTHOR]