• Energy Research
• other engineering and technologies close
• CN close
• IN close

Abstract This study illustrated the pyrolysis of banana peel (BP) as a potential waste management solution. Samples were characterized through Fourier transform infrared spectrometry (FTIR), elemental analysis, and high heating value (HHV) calculation. After pyrolysis experiments were performed at different heating rates (10, 20, 30, and 40 °C min−1) by using a thermogravimetric analyzer coupled with FTIR (TG-FTIR), the apparent activation energies were computed with Friedman, Kissinger–Akahira–Sunose (KAS), and Flynn–Wall–Ozawa (FWO) methods, and the evolved gaseous products were analyzed simultaneously. During pyrolysis, BP underwent three devolatilization steps accompanied by the evolution of some major gaseous products, including CO2, CH4, H2O, CH3COOH, C C, C6H5OH, HCOOH, and CH3CH2OH. Among them, C C, CH3COOH, and CO2 accounted for approximately 71.56% of the total gaseous products. Gas evolution was more significantly influenced by the pyrolysis temperature than by the heating rate. Pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) analysis confirmed the presence of some high-energy compounds and valuable chemicals containing aromatic, aldehyde, ketone, and other functional groups. In terms of preliminary energy balance, more than 70% of the total energy output was attributed to the liquid pyrolytic products followed by the solid and gaseous products. The energy recovery ratio of BP pyrolysis was superior to that of other fuel feedstocks. This work provided insights into resolving environmental problems associated with BP management by pyrolyzing BP as a potential source of renewable bioenergy.

This paper describes an improved algorithm for generalised predictive control (GPC) and applies it to a floor radiant heating system in a full-scale outdoor test-room. The floor heating system is first identified by means of recursive least squares techniques associated with a set of supervision rules for robust system identification. The performance of the floor heating system controlled by GPC, on-off and PI controllers is then evaluated through computer simulations, using the identified models. Experimental results show that the room temperatures predicted with the identified models agree well with the measured data. Simulation results demonstrate that the behaviour of GPC is superior to the other two controllers in terms of response speed, minimum offset and on-off cycling frequency.

Abstract In this study, we propose a novel integrated approach incorporating super-efficiency slack-based measure with undesirable outputs, difference-in-difference and propensity score matching difference-in-difference to explore the effects of carbon trading mechanism on green development efficiency in terms of impact direction, impact degree and impact mode. Taking the provincial data of China from 1999 to 2017 as an example, the empirical results show that China's green development efficiencies have upward, downward and U-shaped trends. Carbon trading mechanism, research and development intensity, and pollution control investment have a positive effect on green development efficiency. However, population capital, urbanization and degree of openness have a negative effect on green development efficiency.

Abstract The reform of the EU ETS markets in 2017 has induced new carbon price forecasting challenges. This study proposes a novel decomposition-ensemble paradigm VMD-GARCH/LSTM-LSTM model to better adapt to the current fast-rising and volatile carbon price. Three significant steps are involved: (1) the Variational Mode Decomposition (VMD) algorithm decomposes the carbon price series into sub-modes; (2) The Long Short-Term Memory (LSTM) network predicts low-frequency sub-modes, with the GARCH model predicting high-frequency sub-modes; (3) the forecasts from sub-modes are ensembled through the LSTM non-linear ensemble method. Combining econometric and artificial intelligence methods, our proposed model has an excellent performance on the current carbon price, with smaller errors than single econometrics or AI models or decomposition-ensemble models with linear simple superposition approaches. VMD have significant advantages over their alternative algorithms. Moreover, the LSTM involved in our model is well suited to forecast the rising carbon price in late EU ETS Phase III, providing good insight into risk aversion for participants.

In this study, a novel framework was proposed by incorporating fuzzy MCDM (multi-criteria decision-making) methods into the SWOT (strength–weakness–opportunity–threat) analysis for diagnosing China’s modern CTC (coal-to-chemical) industry. In the framework, the SWOT analysis was employed to systematically identify the critical factors and then formulate the strategies for promoting the development of this industry. Subsequently, the weights of the factors were accurately determined by using the fuzzy DANP method (decision-making trial and evaluation laboratory-based analytic network process), which tackles the uncertainty in the subjective judgments and the interrelationships among the affecting factors, while the sequence of the strategies was rigorously determined by developing a fusion approach, which reconciles the conflicting rankings derived from four fuzzy MCDM methods for offering a compromised decision. The obtained results were confirmed by performing the sensitivity analysis, which provides two key explanations for the China’s CTC industry. First, two factors from the opportunity perspective, i.e., “clean utilization of coal” and “energy transformation,” and a threat factor of “policy uncertainty” were identified as the most critical factors among the twelve candidates, demonstrating that the external factors (opportunities and threats) play more important roles than the internal drivers (strengths and weaknesses) in affecting the current status of China’s CTC industry. Second, two competitive strategies including drafting national development plan and preferential development of demonstration projects are more favored than the other six strategies, implying that the measures by taking advantage of the strengths to avoid the threats could be effective to promote the development of CTC in China.

Abstract Considering the sustainable energy strategy for buildings, external wall insulation system is expected to play an important role in building energy conversation. And higher energy efficiency drives our demands for much thicker thermal insulation materials. However, it does not mean that the thicker the better, especially considering various requirements and properties. Among these are materials mechanical strength, aging durability, water resistance, construction difficulty and even fire safety performance. In the following paper to explore fire safety of organic thermal insulation, polyurethane foam (PUF) and extruded polystyrene (XPS) were selected to carry out a series of lab-scale tests over a wide range of widths. What is more, some fire safety aspects have been studied and compared: temperature variation in solid and gas phase, heat and mass transfer process, flame propagation over material surface, flame height, material melting and charring, etc. The aim of exploring relationship between material flammability and width is to help to select a proper width when considering fire safety, and lay a foundation for us to conduct real fire disaster of external insulation system in following study.

Abstract A critical aspect of the design, simulation, control, and optimization of Photovoltaic (PV) systems is evaluating the PV model's optimal parameter values based on the actual measured voltage and current values. To address that concern, an enhanced spherical evolution algorithm with no metaphor is proposed for identifying unknown parameters in PV models. The algorithm combines Laplace's cross search mechanism (LCS) and the Nelder-Mead simplex method (NMs), called LCNMSE. In a sense, the goal of LCS is to enrich the diversity of solution sets and make the variety of solutions coarser. The NMs enhances the algorithm exploitation by further scanning more promising ranges in the local region. This idea is developed to improve the local optimal solution's accuracy. In conjunction with both, a balance between exploration and exploitation is maintained. To verify the effectiveness of LCNMSE on high and multi-peaks cases, it is compared with eight state-of-the-art and basic algorithms based on 28 benchmark functions selected from 23 benchmark functions and 30 IEEE CEC2014 benchmark problems. Then, the method is utilized to evaluate the solar cells' parameters and PV modules. Experiments show that the algorithm performs well in evaluating different PV models' unknown parameters than other existing algorithms. Therefore, LCNMSE is an accurate and efficient technique for solar cell and PV models' parameter extraction problems. For further info or any question on metaphor-free LCNMSE, please visit https://aliasgharheidari.com .

This review aims to present an analysis and discussion on the processing of lignocellulosic biomass in terms of biorefinery concept and circular bioeconomy operating at high solids lignocellulosic (above 15% [w/w]) at the pretreatment, enzymatic hydrolysis stage, and fermentation strategy for an integrated lignocellulosic bioprocessing. Studies suggest high solids concentration enzymatic hydrolysis for improved sugars yields and methods to overcome mass transport constraints. Rheological and computational fluid dynamics models of high solids operation through evaluation of mass and momentum transfer limitations are presented. Also, the review paper explores operational feeding strategies to obtain high ethanol concentration and conversion yield, from the hydrothermal pretreatment and investigates the impact of mass load over the operational techniques. Finally, this review contains a brief overview of some of the operations that have successfully scaled up and implemented high-solids enzymatic hydrolysis in terms of the biorefinery concept.