4.7 Article

Energy analysis and resources optimization of complex chemical processes: Evidence based on novel DEA cross-model

期刊

ENERGY
卷 218, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.119508

关键词

Energy saving; Resources optimization; DEA cross-Model; Interpretative structural model; Analytic hierarchy process; Complex chemical processes

资金

  1. National Natural Science Foundation of China [21978013, 61673046]
  2. Fundamental Research Funds for the Central Universities [XK1802-4]
  3. National Key Research and Development Program of China [2019YFB1503904]
  4. Science and Technology Major Project of Guizhou Province (Guizhou Branch) [[2018]3002]

向作者/读者索取更多资源

By utilizing a novel energy analysis and resource optimization model based on data envelopment analysis, interpretative structural model, and analytic hierarchy process, the proposed method can simplify input indicators and improve production efficiency in complex chemical processes.
Improving production efficiency and optimizing resources can accelerate the sustained and stable development of complex chemical processes. This paper presents novel energy analysis and resource optimization model based on data envelopment analysis cross-model integrated interpretative structural model and analytic hierarchy process to effectively simplify input indicators. The interpretative structural model can divide production data with many uncertain dimensions into some sub-elements with obvious hierarchical relationships. Then the sub-elements in the same layer are merged into a major element by the analytic hierarchy process method for further simplifying input indicators of the improved DEACM. At last, the proposed method is used to build the energy analysis and resource optimization model for PTA and ethylene production systems in complex chemical processes. In our experiments, the difference in efficiency values obtained by the proposed model is more significant and accurate. In addition, it can optimize production efficiency and guide ineffective production processes. The electrical conductivity of the purified terephthalic acid systems can reduce by 0.47%. And the ethylene production of the ethylene production systems can increase by 3.93%, and the carbon emissions of the ethylene production system can reduce by 25,297 tons approximately. (C) 2020 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Thermodynamics

Energy supply efficiency evaluation of integrated energy systems using novel SBM-DEA integrating Monte Carlo

Di Cong, Lingling Liang, Shaoxing Jing, Yongming Han, Zhiqiang Geng, Chong Chu

Summary: This study proposes an energy supply efficiency evaluation model for integrated energy systems based on SBM-DEA and Monte Carlo to achieve energy optimization and carbon tax reduction effectively. It has a high degree of discrimination and can obtain better effective decision-making units, leading to the successful achievement of energy optimization and carbon tax reduction in integrated energy systems.

ENERGY (2021)

Article Agricultural Economics & Policy

Risk early warning of food safety using novel long short-term memory neural network integrating sum product based analytic hierarchy process

Zhiqiang Geng, Lingling Liang, Yongming Han, Guangcan Tao, Chong Chu

Summary: This paper proposes a novel risk early warning modelling method based on the LSTM neural network and AHP-SP, which shows higher accuracy in predicting the development trend of food safety risk compared to traditional methods. The method can provide decision-making basis for relevant departments to formulate targeted risk prevention and control measures.

BRITISH FOOD JOURNAL (2022)

Article Automation & Control Systems

Novel variation mode decomposition integrated adaptive sparse principal component analysis and it application in fault diagnosis

Zhiqiang Geng, Xiaoyan Duan, Yongming Han, Fenfen Liu, Wei Xu

Summary: Sparse principal component analysis (SPCA) is widely used in fault detection for complex chemical processes. However, it has limitations in data processing, fixed models, and single fault classification in time-varying processes. Therefore, an adaptive SPCA algorithm fused with improved variation mode decomposition (ASPCA-IVMD) is proposed for fault detection in chemical processes.

ISA TRANSACTIONS (2022)

Article Automation & Control Systems

Novel Transformer Based on Gated Convolutional Neural Network for Dynamic Soft Sensor Modeling of Industrial Processes

Zhiqiang Geng, Zhiwei Chen, Qingchao Meng, Yongming Han

Summary: In this article, a novel Gated Convolutional neural network-based Transformer (GCT) is proposed for dynamic soft sensor modeling of industrial processes. The GCT encodes short-term patterns, filters important features adaptively, models the correlation between moments using multihead attention mechanism, and obtains prediction results through a linear neural network layer. Experimental results show that the proposed method achieves state-of-the-art performance in the dynamic soft sensor modeling of industrial processes.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

Article Automation & Control Systems

Short-Time Wavelet Entropy Integrating Improved LSTM for Fault Diagnosis of Modular Multilevel Converter

Yongming Han, Wang Qi, Ning Ding, Zhiqiang Geng

Summary: This article presents a fault diagnosis method based on short-time wavelet entropy integrating LSTM and SVM to extract and process fault information in MMC system, achieving accurate and robust fault diagnosis of multiple fault types.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

Article Automation & Control Systems

Risk prediction model for food safety based on improved random forest integrating virtual sample

Zhiqiang Geng, Xiaoyan Duan, Jiatong Li, Chong Chu, Yongming Han

Summary: Food safety has a significant impact on the world economy and global health, and improving the accuracy of risk prediction and prevention is crucial for sustainable development. This paper proposes a food safety risk prediction model based on an improved random forest method and the Monte Carlo algorithm to enhance prediction accuracy and ensure personnel safety. The model outperforms existing techniques and provides decision-making assistance for preventing and controlling food risk events.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2022)

Article Automation & Control Systems

An intelligent moving window sparse principal component analysis-based case based reasoning for fault diagnosis: Case of the drilling process

Yongming Han, Jintao Liu, Fenfen Liu, Zhiqiang Geng

Summary: This paper proposes an intelligent moving window based sparse principal component analysis integrating case-based reasoning method for fault diagnosis in the drilling process of the petrochemical industry. Experimental results demonstrate that the method effectively reduces risks and costs.

ISA TRANSACTIONS (2022)

Article Thermodynamics

Novel production prediction model of gasoline production processes for energy saving and economic increasing based on AM-GRU integrating the UMAP algorithm

Jintao Liu, Liangchao Chen, Wei Xu, Mingfei Feng, Yongming Han, Tao Xia, Zhiqiang Geng

Summary: This paper proposes a novel production prediction model using an attention mechanism and gated recurrent unit, which improves the accuracy and stability of gasoline production. By processing the collected data and analyzing the correlations, the performance of the model is further optimized.

ENERGY (2023)

Article Automation & Control Systems

Novel Feature-Disentangled Autoencoder Integrating Residual Network for Industrial Soft Sensor

Hao Wu, Yongming Han, Qunxiong Zhu, Zhiqiang Geng

Summary: A novel feature-disentangled AE (FDAE) integrating Resnet is proposed to improve the low robustness and weak generalization of existing deep AE for soft sensor modeling. The FDAE can obtain disentangled multisource features through a trend-periodic LSTM and a dynamic self-attention CNN, and the Resnet is used to establish the relationship between these features and outputs. Experimental results demonstrate that the FDAE-Resnet outperforms other state-of-the-art methods in melt index modeling, reducing the root mean square error by 26.2% and the mean absolute percentage error by 38.2% on average in changed working conditions.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2023)

Article Computer Science, Artificial Intelligence

A novel pedal musculoskeletal response based on differential spatio-temporal LSTM for human activity recognition

Hao Wu, Zhichao Zhang, Xiaoyong Li, Kai Shang, Yongming Han, Zhiqiang Geng, Tingrui Pan

Summary: In recent years, HAR with wearable devices has been widely used for everyday life tracking and healthcare monitoring. This study proposes a HAR system based on a pedal wearable device, which overcomes the challenges of weak sensor durability and difficulty capturing dynamic features of traditional devices. The system utilizes a novel DST-LSTM method and obtains pedal musculoskeletal response data to classify activity statuses using multi-head graph attention networks and a spatial gate.

KNOWLEDGE-BASED SYSTEMS (2023)

Article Automation & Control Systems

Intelligent Small Sample Defect Detection of Water Walls in Power Plants Using Novel Deep Learning Integrating Deep Convolutional GAN

Zhiqiang Geng, Chunjing Shi, Yongming Han

Summary: This research proposes a deep learning method that combines deep convolutional generative adversarial networks (DCGAN) and a seam carving algorithm to address the issue of small sample defect detection. The method is applied to the defect detection of water walls in an actual thermal power generation plant, achieving a detection accuracy of 98.43%, surpassing other methods and demonstrating the best generalization ability.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2023)

Article Computer Science, Artificial Intelligence

Novel IAPSO-LSTM neural network for risk analysis and early warning of food safety

Zhiqiang Geng, Xintian Wang, Yuangang Jiang, Yongming Han, Bo Ma, Chong Chu

Summary: This paper proposes an improved adaptive particle swarm optimization algorithm (IAPSO) for optimizing the long short-term memory (LSTM) neural network (IAPSO-LSTM) to develop a food safety risk early warning model. The proposed IAPSO algorithm is compared with traditional PSO, classic PSO, adaptive PSO, and deterministic and adaptive PSO based on five benchmark functions, showing the best convergence speed and precision. The risk value of food safety detection data is obtained using the analytic hierarchy process, and the IAPSO algorithm is used to optimize the LSTM's hyperparameters. The model is evaluated using composite seasoning detection data, demonstrating superior performance and the ability to effectively warn potential food safety risks.

EXPERT SYSTEMS WITH APPLICATIONS (2023)

Article Automation & Control Systems

A Novel Wrapped Feature Selection Framework for Developing Power System Intrusion Detection Based on Machine Learning Methods

Yongming Han, Yue Wang, Yuan Cao, Zhiqiang Geng, Qunxiong Zhu

Summary: This article proposes a novel binary particle swarm-wrapped feature selection optimization framework (BPSWO), which can improve the intrusion detection accuracy of machine learning methods. The proposed method is examined on the public power system from Oak Ridge National Laboratory, USA and the IEEE 57-bus system. Experimental results show that the BPSWO can achieve the state-of-the-art in the detection accuracy, proving the effectiveness and stability of the proposed method.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2023)

Article Engineering, Electrical & Electronic

Robust Low-Rank Clustering Contrastive Learning Integrating Transformer for Noisy Industrial Soft Sensors

Hao Wu, Yongming Han, Min Liu, Zhiqiang Geng

Summary: In this article, a novel robust low-rank clustering contrastive learning (LrCCL-T) method is proposed to learn intrinsic and invariant feature representations from process data. The LrCCL-T integrates Lr prior and adaptive CCL to enhance the learned feature representations. The transformer is used to build the soft sensor model and extract dynamic temporal relationship between the learned features and outputs. Experimental results on industrial datasets demonstrate the effectiveness and robustness of the proposed method.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2023)

Article Engineering, Electrical & Electronic

Temporal Chain Network With Intuitive Attention Mechanism for Long-Term Series Forecasting

Zhen Zhang, Yongming Han, Bo Ma, Min Liu, Zhiqiang Geng

Summary: This article proposes a novel temporal chain network (TCNet) for long-term series forecasting (LTSF). By constructing a one-way chain graph neural network (GNN) and introducing an intuitive attention mechanism, the TCNet avoids the issue of temporal information loss in transformer-based LTSF methods. Experimental results demonstrate that the TCNet outperforms current baselines on multiple benchmark datasets. Moreover, the article suggests that the bottleneck of transformer-based LTSF methods mainly stems from the complex architecture of the decoder.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2023)

Article Thermodynamics

Experimental investigation of a dual-pontoon WEC-type breakwater with a hydraulic-pneumatic complementary power take-off system

Yong Cheng, Fukai Song, Lei Fu, Saishuai Dai, Zhiming Yuan, Atilla Incecik

Summary: This paper investigates the accessibility of wave energy absorption by a dual-pontoon floating breakwater integrated with hybrid-type wave energy converters (WECs) and proposes a hydraulic-pneumatic complementary energy extraction method. The performance of the system is validated through experiments and comparative analysis.

ENERGY (2024)

Article Thermodynamics

Site selection decision for biomass cogeneration projects from a sustainable perspective: A case study of China

Jing Gao, Chao Wang, Zhanwu Wang, Jin Lin, Runkai Zhang, Xin Wu, Guangyin Xu, Zhenfeng Wang

Summary: This study aims to establish a new integrated method for biomass cogeneration project site selection, with a focus on the application of the model in Henan Province. By integrating Geographic Information System and Multiple Criterion Decision Making methods, the study conducts site selection in two stages, providing a theoretical reference for the construction of biomass cogeneration projects.

ENERGY (2024)

Article Thermodynamics

Development of a hybridized small modular reactor and solar-based energy system for useful commodities required for sustainable cities

Mert Temiz, Ibrahim Dincer

Summary: The current study presents a hybrid small modular nuclear reactor and solar-based system for sustainable communities, integrating floating and bifacial photovoltaic arrays with a small modular reactor. The system efficiently generates power, hydrogen, ammonia, freshwater, and heat for residential, agricultural, and aquaculture facilities. Thermodynamic analysis shows high energy and exergy efficiencies, as well as large-scale ammonia production meeting the needs of metropolitan areas. The hybridization of nuclear and solar technologies offers advantages of reliability, environmental friendliness, and cost efficiency compared to renewable-alone and fossil-based systems.

ENERGY (2024)

Editorial Material Thermodynamics

ENERGY special issue devoted to the 7th international conference CPOTE2022

Wojciech Stanek, Wojciech Adamczyk

ENERGY (2024)

Article Thermodynamics

Investigating the influence of outdoor temperature variations on fire-induced smoke behavior in an atrium-type underground metro station using hybrid ventilation systems

Desheng Xu, Yanfeng Li, Tianmei Du, Hua Zhong, Youbo Huang, Lei Li, Xiangling Duanmu

Summary: This study investigates the optimization of hybrid mechanical-natural ventilation for smoke control in complex metro stations. The results show that atrium fires are more significantly impacted by outdoor temperature variations compared to concourse/platform fires. The gathered high-temperature smoke inside the atrium can reach up to 900 K under a 5 MW train fire energy release. The findings provide crucial engineering insights into integrating weather data and adaptable ventilation protocols for smoke prevention/mitigation.

ENERGY (2024)

Article Thermodynamics

In-situ pressure-preserved coring for deep oil and gas exploration: Design scheme for a coring tool and research on the in-situ pressure-preserving mechanism

Da Guo, Heping Xie, Mingzhong Gao, Jianan Li, Zhiqiang He, Ling Chen, Cong Li, Le Zhao, Dingming Wang, Yiwei Zhang, Xin Fang, Guikang Liu, Zhongya Zhou, Lin Dai

Summary: This study proposes a new in-situ pressure-preserved coring tool and elaborates its pressure-preserving mechanism. The experimental and field test results demonstrate that this tool has a high pressure-preservation capability and can maintain a stable pressure in deep wells. This study provides a theoretical framework and design standards for the development of similar technologies.

ENERGY (2024)

Article Thermodynamics

How coal de-capacity policy affects renewable energy development efficiency? Evidence from China

Aolin Lai, Qunwei Wang

Summary: This study assesses the impact of China's de-capacity policy on renewable energy development efficiency (REDE) using the Global-MSBM model and the difference-in-differences method. The findings indicate that the policy significantly enhances REDE, promoting technological advancements and marketization. Moreover, regions with stricter environmental regulations experience a higher impact.

ENERGY (2024)

Article Thermodynamics

Performance improvement of microbial fuel cell using experimental investigation and fuzzy modelling

Mostafa Ghasemi, Hegazy Rezk

Summary: This study utilizes fuzzy modeling and optimization to enhance the performance of microbial fuel cells (MFCs). By simulating and analyzing experimental data sets, the ideal parameter values for increasing power density, COD elimination, and coulombic efficiency were determined. The results demonstrate that the fuzzy model and optimization methods can significantly improve the performance of MFCs.

ENERGY (2024)

Article Thermodynamics

A novel prediction method of fuel consumption for wing-diesel hybrid vessels based on feature construction

Zhang Ruan, Lianzhong Huang, Kai Wang, Ranqi Ma, Zhongyi Wang, Rui Zhang, Haoyang Zhao, Cong Wang

Summary: This paper proposes a grey box model for fuel consumption prediction of wing-diesel hybrid vessels based on feature construction. By using both parallel and series grey box modeling methods and six machine learning algorithms, twelve combinations of prediction models are established. A feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is introduced. The best combination is obtained by considering the root mean square error, and it shows improved accuracy compared to the white box model. The proposed grey box model can accurately predict the daily fuel consumption of wing-diesel hybrid vessels, contributing to operational optimization and the greenization and decarbonization of the shipping industry.

ENERGY (2024)

Article Thermodynamics

Off-farm employment and household clean energy transition in rural China: A study based on a gender perspective

Huayi Chang, Nico Heerink, Junbiao Zhang, Ke He

Summary: This study examines the interaction between off-farm employment decisions between couples and household clean energy consumption in rural China, and finds that two-paycheck households are more likely to consume clean energy. The off-farm employment of women is a key factor driving household clean energy consumption to a higher level, with wage-employed wives having a stronger influence on these decisions than self-employed ones.

ENERGY (2024)

Article Thermodynamics

A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data

Hanguan Wen, Xiufeng Liu, Ming Yang, Bo Lei, Xu Cheng, Zhe Chen

Summary: Demand-side management is crucial to smart energy systems. This paper proposes a data-driven approach to understand the relationship between energy consumption patterns and household characteristics for better DSM services. The proposed method uses a clustering algorithm to generate optimal customer groups for DSM and a deep learning model for training. The model can predict the possibility of DSM membership for a given household. The results demonstrate the usefulness of weekly energy consumption data and household socio-demographic information for distinguishing consumer groups and the potential for targeted DSM strategies.

ENERGY (2024)

Article Thermodynamics

Study on the heat recovery behavior of horizontal well systems in the Qiabuqia geothermal area of the Gonghe Basin, China

Xinglan Hou, Xiuping Zhong, Shuaishuai Nie, Yafei Wang, Guigang Tu, Yingrui Ma, Kunyan Liu, Chen Chen

Summary: This study explores the feasibility of utilizing a multi-level horizontal branch well heat recovery system in the Qiabuqia geothermal field. The research systematically investigates the effects of various engineering parameters on production temperature, establishes mathematical models to describe their relationships, and evaluates the economic viability of the system. The findings demonstrate the significant economic feasibility of the multi-level branch well system.

ENERGY (2024)

Article Thermodynamics

Role of tip leakage flow in an ultra-highly loaded transonic rotor aerodynamics

Longxin Zhang, Songtao Wang, Site Hu

Summary: This investigation reveals the influence of tip leakage flow on the modern transonic rotor and finds that the increase of tip clearance size leads to a decline in rotor performance. However, an optimal tip clearance size can extend the rotor's stall margin.

ENERGY (2024)

Article Thermodynamics

Fifth-generation district heating and cooling: Opportunities and implementation challenges in a mild climate

Kristian Gjoka, Behzad Rismanchi, Robert H. Crawford

Summary: This paper proposes a framework for assessing the performance of 5GDHC systems and demonstrates it through a case study in a university campus in Melbourne, Australia. The results show that 5GDHC systems are a cost-effective and environmentally viable solution in mild climates, and their successful implementation in Australia can create new market opportunities and potential adoption in other countries with similar climatic conditions.

ENERGY (2024)

Article Thermodynamics

An orientation-adaptive electromagnetic energy harvester scavenging for wind-induced vibration

Jianwei Li, Guotai Wang, Panpan Yang, Yongshuang Wen, Leian Zhang, Rujun Song, Chengwei Hou

Summary: This study proposes an orientation-adaptive electromagnetic energy harvester by introducing a rotatable bluff body, which allows for self-regulation to cater for changing wind flow direction. Experimental results show that the output power of the energy harvester can be greatly enhanced with increased rotatory inertia of the rotating bluff body, providing a promising solution for harnessing wind-induced vibration energy.

ENERGY (2024)