Energy Conversion and Management: X

In this study, an integrated adiabatic operated chemical looping combustion and supercritical CO2 Brayton cycle using biomass as feedstock was studied based on the thermodynamic analysis. Based on comparisons of species and adiabatic flame temperature in the product gas, it was found that high air and oxygen carrier flow rates are required in adiabatic chemical looping combustion for obtaining the results from the conventional combustion under adiabatic operation. Using the product gas from the fuel reactor and flue gas from the air reactor as heat sources for the simple recuperated supercritical CO2 Brayton cycle, it is found that the cycle efficiency increases with increased turbine inlet temperature and decreased condenser exit temperature under various compressor discharge pressures. The overall thermal efficiency of the integrated system, defined as the ratio of total net work of the power cycle to the higher heating value of biomass, depends on the CO2 mass flow rates in the power cycles and also increases with increased compressor discharged pressures. The maximum cycle and overall thermal efficiencies are found to be 51% and 21%, respectively.

Rei-Yu Chein

Wei-Hsin Chen

Energy Conversion and Management X

Thermodynamic analysis of integrated adiabatic chemical looping combustion and supercritical CO2 cycle

集成绝热化学链燃烧与超临界CO2循环的热力学分析

Yohanis Tangke Tosuli

None Cahyadi

Hafif Dafiqurrohman

Rudi Hermawan

Adi Surjosatyo

Gasification of sago dreg waste in a top-lit updraft fixed bed gasifier: Syngas composition and its effect with additional Al2O3 as catalyst

顶燃式固定床气化炉中西米渣废料的气化：合成气组成及其在添加Al2O3作为催化剂时的影响

The proportion of wind energy in global energy structure is growing rapidly, promoting the development of wind power forecasting (WPF) technologies to solve the uncertainty and intermittence of wind power generation. However, the nonlinear and stochastic features of wind power time series restrain the accuracy of multi-step prediction performance. A multi-step WPF (MS-WPF) approach based on a time series bi-level empirical mode decomposition (BLEMD) method and BiLSTM neural network is proposed in this paper to improve the WPF accuracy of regional wind power generators. Since the uncertainty is always generated through coupled factors from both wind and weather-to-power conversion, the linearity feature is first introduced as an aspect apart from the frequency in the proposed approach to decompose the wind power time sequence data. The proposed BLEMD introduces Pearson product-moment correlation coefficient to evaluate the linearity of time series and a linearity-based decomposition algorithm is designed accordingly. To further enhance the precision and release computation burdens, a DL-based prediction strategy, including a BiLSTM network, a CNN-BiLSTM network, and a mean weight estimation method are implemented to predict the components separately. The proposed method only relies on local data, greatly reducing the data acquisition and computation cost. The precision of the proposed MS-WPF is verified by a 2.5kW wind turbine with horizons from 5 seconds to 30 seconds, a 1.5MW wind turbine with horizons from 10 minutes to 1 hour, and a 51MW wind farm with horizons from 1 hour to 6 hours. The comparative experimental results with other cutting-edge methods indicated that the proposed MS-WPF has superior prediction accuracy and stable performance for multi-step prediction.

Jingxuan Wu

Shuting Li

Juan C. Vasquez

Josep M. Guerrero

A Bi-level Mode Decomposition Framework for Multi-step Wind Power Forecasting using Deep Neural Network

基于深度神经网络的多步风电预测的双层模式分解框架

Phase Change Materials (PCMs) encapsulated inside different shape and size enclosures have been playing an important role in designing thermal energy storage (TES) systems for a wide range of applications. In the present work, transient heat transfer and the melting process of n-octadecane PCM encapsulated in a novel Pear-Shaped Thermal Energy Storage (PS-TES) system with and without constraint are numerically investigated and verified with experimental visualizations. An adiabatic cylindrical rod, placed at the axis of symmetry of the pear-shaped enclosure, is used to create the constraint. A mathematical model is developed and numerically solved to study energy transport processes inside the proposed PS-TES systems. The heat transfer characteristics such as melt fraction, Nusselt number, and energy stored in the system and their temporal variation during the melting process are determined. The melting process is visualized numerically to track the solid-liquid interface during the melting process as well. Comparison of results from the unconstrained and constrained cases reveals that the existence of the adiabatic constraint inside the system decreases the melting rate, as the total time required to complete the melting process in the constrained melting (~178 min) is almost twice that of unconstrained melting (~97 min). The effect of the Rayleigh number on the melt fraction, Nusselt number, and the stored energy is studied and discussed as well. Furthermore, a comparison between the melt fraction results for pear-shaped system and a convectional cylindrical container with the same height and same volume shows that the complete melting time for the PS-TES system (~97 min) is less compared to the one for the cylindrical case (~108 min). A comprehensive experimental setup is also developed using a constant temperature bath and thermal regulator to visualize melting images and track the melting front during the phase change process. Numerical images of heat transfer field and solid-liquid interface, as well as the temporal variation of melt fraction in both test cases, are compared with experimental visualizations, and an excellent agreement is reported.

M.R. Mohaghegh

Y. Alomair

M. Alomair

S.H. Tasnim

S. Mahmud

H. Abdullah

Melting of PCM inside a novel encapsulation design for thermal energy storage system

新型封装设计中相变材料熔化用于储热系统

Assmaa Abd-Elmonem

Zill E Shams

Mariam Imtiaz

Kashif Ali

Sohail Ahmad

Wasim Jamshed

Fayza Abdel Aziz ElSeabee

Neissrien Alhubieshi

Syed M. Hussain

Hijaz Ahmad

Flow dynamics in a vertical pipe with internal fins exposed to sunlight – A machine learning based evaluation of thermal signature

阳光照射下带内鳍垂直管的流动特性——基于机器学习的温度特征评估

Sabah Mariyam

Mohammad Alherbawi

Gordon McKay

Tareq Al-Ansari

A predictive model for biomass waste pyrolysis yield: Exploring the correlation of proximate analysis and product composition

生物质废弃物热解产率的预测模型：探索工业分析与产物组成的相关性

Ahmed Lasheen

Hatem F. Sindi

Hatem H. Zeineldin

Mohamed Morgan

Online stability assessment for isolated microgrid via LASSO based neural network algorithm

基于LASSO神经网络算法的孤立微电网在线稳定性评估

Ahmad K. ALAhmad

Renuga Verayiah

Saleh Ba-swaimi

Hussain Shareef

Agileswari Ramasam

Azzam Abu-Rayash

Optimized Two-Stage planning model for integrating Compressed air energy storage with uncertain correlated wind farms in power systems

优化两阶段规划模型用于整合电力系统中具有不确定相关性的风电场与压缩空气储能

Agileswari Ramasamy

Optimizing renewable energy and green technologies in distribution systems through stochastic planning of distributed energy resources

通过分布式能源的随机规划优化配电系统中的可再生能源和绿色技术

Abbreviation	Energy Convers. Manage.: X
Journal Impact	7.20
Quartiles(Global)	THERMODYNAMICS(Q1)

ISSN	2590-1745
h-index	31

Publication Information	Publisher: Elsevier Ltd，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2019，Proportion of original research papers: 94.03%，Self Citation Rate:5.60%， Gold OA Rate: 98.58%
Journal Fees	$2170Details
Average review cycle	16 weeks

Energy Conversion and Management: X

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Thermodynamic analysis of integrated adiabatic chemical looping combustion and supercritical CO2 cycle

Gasification of sago dreg waste in a top-lit updraft fixed bed gasifier: Syngas composition and its effect with additional Al2O3 as catalyst

A Bi-level Mode Decomposition Framework for Multi-step Wind Power Forecasting using Deep Neural Network

Melting of PCM inside a novel encapsulation design for thermal energy storage system

Flow dynamics in a vertical pipe with internal fins exposed to sunlight – A machine learning based evaluation of thermal signature

A predictive model for biomass waste pyrolysis yield: Exploring the correlation of proximate analysis and product composition

Flow dynamics in a vertical pipe with internal fins exposed to sunlight – A machine learning based evaluation of thermal signature

Online stability assessment for isolated microgrid via LASSO based neural network algorithm

Optimized Two-Stage planning model for integrating Compressed air energy storage with uncertain correlated wind farms in power systems

Optimizing renewable energy and green technologies in distribution systems through stochastic planning of distributed energy resources