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Abstract The purpose of this paper is to investigate the influence of blending methods on the co-gasification of petroleum coke and lignite with CO 2 using a thermogravimetric system at 0.1 MPa. The weight loss curves, XRD analysis, SEM images, BET specific surface area, were investigated. It was observed that petroleum coke shows a low reactivity because of the graphitic carbon structure, low catalyst content and small specific surface area. Blending with lignite can get a high reactivity. The co-gasification reactivity was significantly influenced by blending methods. Wet grinding is much effective than dry grinding. Long grinding time made lignite show greater BET specific area. And the sample in long grinding time has more association chances between petroleum coke and AAEM species. The co-gasification reactivity increases linearly with a rise of BET specific area.

Microalgae are recognized as a third generation feedstock for biofuel production due to their rapid growth rates and lignin-free characteristics. In this study, a lipid extracted microalgal biomass residues was used as the raw material to produce isoprene, α-pinene and β-pinene with an engineered E. coli strain. We adopted an optimal sulfuric acid hydrolysis method (1:7 ratio of solid to acid solution, 32% (w/v) concentration of sulfuric acid solution at 90 °C for 90 min) to efficiently convert holocellulose into glucose efficiently (6.37 g/L). Futhermore, we explored a novel detoxification strategy (phosphoric acid/calcium hydroxide) to remove inhibitors and notably acetic acid, furfural and 5-hydroxymethylfurfural (5-HMF) were reduced by 5.32%, different number given later 99.19% and 98.22%, respectively. Finally, the fermentation concentrations of isoprene (223.23 mg/L), α-pinene (382.21 μg/L) and β-pinene (17.4 mg/L) were achieved using the detoxified hydrolysate as the carbon source, equivalent to approximately 86.02%, 90.16% and 88.32% of those produced by the engineered E. coli strain fermented on pure glucose, respectively.

The shipping industry faces a large challenge as it needs to significantly lower the amounts of Green House Gas emissions. Traditionally, reducing the fuel consumption for ships has been achieved during the design stage and, after building a ship, through optimisation of ship operations. In recent years, ship efficiency improvements using Machine Learning (ML) methods are quickly progressing, facilitated by available data from remote sensing, experiments and high-fidelity simulations. The data have been successfully applied to extract intricate empirical rules that can reduce emissions thereby helping achieve green shipping. This article presents an overview of applying ML techniques to enhance ships’ sustainability. The work covers the ML fundamentals and applications in relevant areas: ship design, operational performance, and voyage planning. Suitable ML approaches are analysed and compared on a scenario basis, with their space for improvements also discussed. Meanwhile, a reminder is given that ML has many inherent uncertainties and hence should be used with caution.

This is a postprint version of the Book Chapter. Information regarding the official publication is available from the link below - Copyright @ 2011 Springer Implementation of a Life Cycle Sustainability Management (LCSM) strategy can involve significant challenges because of competing or conflicting objectives between stakeholders. These differences may, if not identified and managed, hinder successful adoption of sustainability initiatives. This article proposes a conceptual framework for stakeholder management in a LCSM context. The framework identifies the key sustainability stakeholder groups and suggests strategic ambiguity as a management tool to harness dysfunctional conflict into constructive collaboration. The framework is of practical value as it can be used as a guideline by managers who wish to improve collaboration with stakeholders along the supply chain. The article also fills a gap in the academic literature where there is only limited research on sustainability stakeholder management through strategic ambiguity.

Plug-in hybrid electric vehicles (PHEVs) have a larger battery and can replace a certain amount of conventional fossil fuels with grid electricity, which differs from the traditional hybrid electric vehicles (HEVs). The application of the onboard electrical energy significantly influences the energy utilization efficiency and thus impacts the fuel economy. In this paper, the basic PHEV operation modes are defined as pure electric driving (PED), hybrid driving charge depleting (HDCD) and hybrid driving charge sustaining (HDCS) based on the battery state of charge (SoC) profile. For a plug-in hybrid electric bus (PHEB), three different energy management strategies, which are combined with two or three of the basic operation modes, are put forward and comparatively examined based on simulation models. If some trip information can be approximately known in advance such as the trip distance and the mean power demand, the PED + HDCD + HDCS strategy comprised optimally of the PED mode, the HDCD mode and the HDCS mode would be the best energy management strategy.

Abstract The improvement of thermodynamic efficiency of power plants is of great interest for the whole energy industry. The use of Kalina cycle has a great potential to improve the thermal efficiency of a nuclear power plant. This cycle uses a mixture of ammonia and water as working fluid. In this paper, we discuss the development of an Ammonia-Water mixture Property Code (AWProC). The estimation of the mixture properties are based on the Gibbs free energy functions. The code is verified and validated against experimental data available in the literature and REFPROP code. It is shown that AWProC can accurately estimate the thermodynamic properties of ammonia-water mixtures over a wide range of conditions, including high temperature and pressure regions. The code is then used to investigate the feasibility of applying the Kalina cycle to a typical Pressurizer Water Reactor (PWR) plant as an effective way to improve the plant efficiency. The fundamental of Basic-Kalina (B-K) cycle is described in detail firstly. Then, two modified configurations, Recuperation-Kalina (R-K) and Flash-Kalina (F-K) cycles respectively, are proposed for a typical 1000 MWe PWR. The simulation results indicate that the R-K type cycle can reach about 31.2% efficiency with simple equipment requirements, while the F-K type cycle can reach efficiencies up to about 34.8%, but at the expenses of a slightly more complex design. The present work demonstrates the applicability of the Kalina cycle as a way to improve the thermal efficiency of a nuclear power plant. This concept is meaningful for improving nuclear power plants economic and competitiveness.

Purpose This paper aims to propose a liquidity management solution for Islamic financial institutions (IFIs) that concurs with sustainable development and financial stability. Design/methodology/approach The study is a qualitative research. It uses the exploratory research methodology, specifically the content analysis approach, to gather primary data and identify and interpret relevant secondary data and Sharīʿah concepts. The purpose is to develop a liquidity management solution for IFIs. The proposal is based on the Unleveraged Green Investment Trust (UGIT) model, which is consistent with Basel III regulatory requirements. In developing the UGIT model, the exploratory research was complemented by a case study to examine the UGIT solution for the particular case of renewable energy. Findings The model demonstrates how financial innovation can meet both financial stability and sustainable development objectives, thereby achieving the spirit of Islamic finance. The structure further highlights the importance of regulatory and fiscal frameworks to enhance liquidity management and investor appeal for green financial instruments. Originality/value This study suggests a structure of UGIT to enable IFIs to meet their liquidity management needs while promoting sustainable development.