Computers & Chemical Engineering最新文献_第8页

Alarms prediction and classification in industrial processes using supervised machine learning techniques: A case study in an Algerian gas plant 使用监督机器学习技术的工业过程报警预测和分类：阿尔及利亚天然气厂的案例研究

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-08-27 DOI: 10.1016/j.compchemeng.2025.109378

Samir Sekiou , Ali Behloul , Rachid Nait-Said , Zakarya Chiremsel

{"title":"Alarms prediction and classification in industrial processes using supervised machine learning techniques: A case study in an Algerian gas plant","authors":"Samir Sekiou , Ali Behloul , Rachid Nait-Said , Zakarya Chiremsel","doi":"10.1016/j.compchemeng.2025.109378","DOIUrl":"10.1016/j.compchemeng.2025.109378","url":null,"abstract":"<div><div>Alarm systems are a crucial tool designed to enhance safety levels and ensure the normal functioning of industrial plants, maintaining safe and efficient operations. During industrial process upsets, numerous conflicting and false alarms may trigger simultaneously (alarm floods), leading to confusion and creating significant challenges for operators. These alarm floods affect operators' response time making their intervention extremely difficult. In such abnormal situations, alarm classification and prioritization become crucial, significantly aiding operators by allowing them to promptly and appropriately address safety-critical alarms first, rather than dealing with false or lower-priority alarms. Meanwhile, Machine Learning (ML) is a powerful tool for information extraction that has significantly contributed to knowledge discovery and decision-making. It has been successfully applied in various fields, including fault detection and diagnosis. ML can help address the issue of process alarms by classifying and prioritizing them. This paper presents a Machine Learning-based model (Random Forest) capable of classifying and predicting alarms in industrial processes. Then, it compares its performance to well-known classifiers, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and other supervised machine learning models such as Decision Trees, K-Nearest Neighbors, and Logistic Regression. The performance of these models was rigorously evaluated based on Accuracy, Precision, Recall, F1-Score, and prediction speed. The results from our final simulations show that the RF model achieved the highest Accuracy (98.32%) and F1-Score (0.988), along with a very high Recall (0.987) and precision (0.983). While the RF model demonstrated superior predictive performance in these metrics, it had a slower prediction speed (0.3477 ms per observation) comparing to other models.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109378"},"PeriodicalIF":3.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulation-based techno-economic and environmental evaluation of synthetic natural gas production integrated with sugarcane processing 基于仿真的合成天然气生产与甘蔗加工一体化技术经济环境评价

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-08-25 DOI: 10.1016/j.compchemeng.2025.109367

Ana María Cuezzo , Victoria Olivera , Paula Zulema Araujo , Fernando Daniel Mele

{"title":"Simulation-based techno-economic and environmental evaluation of synthetic natural gas production integrated with sugarcane processing","authors":"Ana María Cuezzo , Victoria Olivera , Paula Zulema Araujo , Fernando Daniel Mele","doi":"10.1016/j.compchemeng.2025.109367","DOIUrl":"10.1016/j.compchemeng.2025.109367","url":null,"abstract":"<div><div>Dependence on fossil fuels is accelerating climate change and has spurred interest in carbon capture and utilization, which converts CO<sub>2</sub> into products such as synthetic natural gas (SNG) that fit seamlessly into existing infrastructure. Despite efficient routes such as biomass gasification and Power-to-Gas, high costs have slowed adoption. In the Argentine sugar industry, biogenic CO<sub>2</sub> from sugarcane processing presents an opportunity to produce low-carbon SNG. This study proposes to evaluate the feasibility of SNG production in sugarcane biorefineries through a combined techno-economic and environmental analysis. By capturing CO<sub>2</sub> from biomass combustion and by combining it with hydrogen, sugarcane-processing facilities could serve as carbon sinks and reduce reliance on fossil fuels, contributing to broader decarbonization goals. Two scenarios are considered, green SNG and gray SNG, which differ in the source of electricity, renewable and current grid electricity, respectively. The green SNG system significantly reduces greenhouse gas emissions and fossil resource use by using renewable H<sub>2</sub> and biogenic CO<sub>2</sub>, while the gray SNG system has a higher environmental impact due to its reliance on the current Argentine fossil-based electricity. The proposed process achieves an overall feedstock conversion of approximately 97%, which supports its technical feasibility. In addition, preliminary cost considerations are presented, outlining the economic challenges for large-scale implementation. The study also notes that the sustainability of SNG production from biogenic CO<sub>2</sub> depends on the carbon intensity of the electricity grid, underlining the importance of transitioning to renewable energy to maximize environmental benefits.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109367"},"PeriodicalIF":3.9,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Circular supply chains for sustainable use of biomass 可持续利用生物质的循环供应链

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-08-25 DOI: 10.1016/j.compchemeng.2025.109368

Maryam Mohammadi , Iiro Harjunkoski

{"title":"Circular supply chains for sustainable use of biomass","authors":"Maryam Mohammadi , Iiro Harjunkoski","doi":"10.1016/j.compchemeng.2025.109368","DOIUrl":"10.1016/j.compchemeng.2025.109368","url":null,"abstract":"<div><div>A significant impediment to sustainable biofuel and bioenergy production is deciding between various supply chain designs, available resources, and process technologies, especially when sustainability is a key criterion. To simultaneously address the challenge of continuous waste generation, increasing energy demand, high natural resource consumption, and increasing greenhouse gas emissions, the use of biobased waste should be enhanced. Providing economical and environmentally sustainable solutions for biowaste processing necessitates developing an optimization model for the associated supply chain network. This study creates a mathematical model to optimally plan and integrate biobased waste supply chain components into a coordinated system. The model aligns the purchasing and supply with varying demands, effectively assigns resources to operations, optimizes the production levels, plans delivery through optimal transportation networks, and reduces operational CO₂ emissions from transport and conversion stages in biomass systems. The results indicate that a well-designed supply chain is more cost-effective and efficient, reduces wasted materials, and keeps up with demand fluctuations. It also diminishes the existing risks and identifies the bottlenecks in the network. Furthermore, decentralized biomass treatment allows localized resource valorization and improves supply chain flexibility by lowering emissions and transportation costs. Using smaller geographically dispersed processing units increases system adaptability and mitigates feedstock variability risks. By optimizing energy efficiency and economic viability, this strategy not only reinforces the circular bioeconomy principles but also makes sustainable biomass consumption more feasible and scalable.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109368"},"PeriodicalIF":3.9,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning framework integrating Transformer and LSTM architectures for pipeline corrosion rate forecasting 一种集成Transformer和LSTM架构的深度学习框架，用于管道腐蚀速率预测

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-08-24 DOI: 10.1016/j.compchemeng.2025.109365

Jianxun Jiang , Xinli Wan , Feng Zhu , Duole Xiang , Ziyan Hu , Shuxing Mu

{"title":"A deep learning framework integrating Transformer and LSTM architectures for pipeline corrosion rate forecasting","authors":"Jianxun Jiang , Xinli Wan , Feng Zhu , Duole Xiang , Ziyan Hu , Shuxing Mu","doi":"10.1016/j.compchemeng.2025.109365","DOIUrl":"10.1016/j.compchemeng.2025.109365","url":null,"abstract":"<div><div>Accurately predicting the corrosion rate is crucial for ensuring the safe operation of buried pipelines. Currently, research on pipeline corrosion prediction is largely confined to static methods, which do not fully capture dynamic safety considerations. In contrast, machine learning techniques can more effectively process experimental data and comprehend its complex characteristics. Based on this, this paper proposes an interpretable Transformer-LSTM (Long Short-Term Memory) model for predicting the corrosion rate of buried pipelines. Its core innovation lies in modifying the Transformer architecture by replacing the decoder layer of the traditional Transformer model with a fully connected layer and substituting the original attention layer with an LSTM layer. This modification allows the model to utilize the storage units of LSTM to effectively store and update information within the sequence. Finally, two cases were combined for case verification. Taking Case 1 as an example, the research results indicate that, compared to the LSTM model, the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) of the Transformer LSTM model are reduced by 85.5 %, 89.8 %, and 83.2 %, respectively. In comparison to the Transformer model, the MAE, MAPE, and RMSE of the Transformer LSTM model decreased by 73.8 %, 80.5 %, and 68.6 %, respectively. Additionally, the SHapley Additive exPlanations (SHAP) method is employed to provide a global and intuitive explanation of the model, aiding in the understanding of the contribution of input features. These research findings will assist pipeline operators in better planning the operation and maintenance of pipelines.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109365"},"PeriodicalIF":3.9,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning-aided optimal design and distributed model predictive control of reactive dividing wall column 反应式分壁塔的机器学习辅助优化设计及分布式模型预测控制

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-08-22 DOI: 10.1016/j.compchemeng.2025.109355

Haohao Zhang , Ping Lu , Chao Hua , Jinyi Chen , Qing Yuan

{"title":"Machine learning-aided optimal design and distributed model predictive control of reactive dividing wall column","authors":"Haohao Zhang , Ping Lu , Chao Hua , Jinyi Chen , Qing Yuan","doi":"10.1016/j.compchemeng.2025.109355","DOIUrl":"10.1016/j.compchemeng.2025.109355","url":null,"abstract":"<div><div>Reactive dividing wall column (RDWC) integrates the advantages of high conversion efficiency, low energy consumption, and reduced investment. However, this further intensification of reaction and separation increases system coupling and significantly complicates process optimization. To address this challenge, this work proposed a machine learning-aided multi-objective optimization (ML-MOO) framework for determining the optimal RDWC design. Taking the dichlorosilane anti-disproportionation RDWC process as a case study, random forest (RF), backpropagation neural network (BPNN), and support vector machine (SVM) were integrated with multi-objective particle swarm optimization (MOPSO) algorithm to optimize the steady-state operating parameters of RDWC. The hyperparameters of three ML models were tuned using Bayesian optimization algorithm (BOA) with 5-fold cross-validation. The results showed that, compared with the rigorous Aspen simulation-based optimization, the SVM surrogate model reduces total annual cost, flow rate of silicon tetrachloride, and environmental impact potential of energy by 5.3 %, 23.5 %, and 7.6 %, respectively, while reducing computation time by 19.3 %. Additionally, due to the existence of internal reactions, the dynamic behavior of RDWC is constrained by both product quality and safety redundancy. To address this, a distributed MPC (DMPC) strategy was proposed, using two sub-MPC controllers to separately control inventory and quality loops, thereby enhancing system fault tolerance. Dynamic response results indicated that benefiting from communication between sub-controllers, the integral absolute error (IAE) value of linear DMPC structure based on linear time-invariant state space (LTI-SS) model differs from that of centralized MPC (CMPC) structure by only 3 % to 10 %, demonstrating similar dynamic response performance while achieving enhanced safety.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109355"},"PeriodicalIF":3.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The BOG dynamic generation model of LNG storage tank considering the SEMP operation 考虑SEMP运行的LNG储罐BOG动态发电模型

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-08-21 DOI: 10.1016/j.compchemeng.2025.109361

Jinyu An, Xiufeng Zhang

{"title":"The BOG dynamic generation model of LNG storage tank considering the SEMP operation","authors":"Jinyu An, Xiufeng Zhang","doi":"10.1016/j.compchemeng.2025.109361","DOIUrl":"10.1016/j.compchemeng.2025.109361","url":null,"abstract":"<div><div>The BOG prediction is the key to designing Liquefied Natural Gas(LNG) storage tanks and Boil-off gas (BOG) recycling. The study on the dynamic law of BOG generation caused by the submerged electric motor pumps(SEMPs) operation is rare. Thus, a dynamic heat transfer model is established based on outer wall heat transfer and submerged pump heat dissipation. We used the fluid volume method (VOF) model to simulate the unloading conditions of cryogenic liquid two-phase flow, and the phase change under static pressure was analyzed. Then, the dynamic heat transfer simulation utilizing the finite element method combined with the steady-state evaporation model was established. The model error is about 1 %, based on testing the actual value of the tank's average temperature and average pressure. Finally, the dynamic BOG generation model is constructed based on the three different filling rates of 10,000 m³storage tanks, namely 80 %, 55 %, and 30 %. The dynamic BOG generation will increase by about 50 % compared with the existing steady-state BOG generation results, and the model has a margin of 10 % errors. The BOG dynamic generation model is more in line with the actual working conditions and is of great significance to energy-saving optimization and reduction of economic losses.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109361"},"PeriodicalIF":3.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven approach to learning optimal forms of constitutive relations in models describing Lithium plating in battery cells 用数据驱动的方法学习描述电池中锂电镀模型中本构关系的最佳形式

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-08-21 DOI: 10.1016/j.compchemeng.2025.109252

Avesta Ahmadi , Kevin J. Sanders , Gillian R. Goward , Bartosz Protas

{"title":"Data-driven approach to learning optimal forms of constitutive relations in models describing Lithium plating in battery cells","authors":"Avesta Ahmadi , Kevin J. Sanders , Gillian R. Goward , Bartosz Protas","doi":"10.1016/j.compchemeng.2025.109252","DOIUrl":"10.1016/j.compchemeng.2025.109252","url":null,"abstract":"<div><div>In this study we construct a data-driven model describing Lithium plating in a battery cell, which is a key process contributing to degradation of such cells. Starting from the fundamental Doyle-Fuller-Newman (DFN) model, we use asymptotic reduction and spatial averaging techniques to derive a simplified representation to track the temporal evolution of two key concentrations in the system, namely, the total intercalated Lithium on the negative electrode particles and total plated Lithium. This model depends on an a priori unknown constitutive relation representing the plating dynamics of the cell as a function of the state variables. An optimal form of this constitutive relation is then deduced from experimental measurements of the time-dependent concentrations of different Lithium phases acquired through Nuclear Magnetic Resonance spectroscopy. This is done by solving an inverse problem in which this constitutive relation is found subject to minimum assumptions as a minimizer of a suitable constrained optimization problem where the discrepancy between the model predictions and experimental data is minimized. This optimization problem is solved using a state-of-the-art adjoint-based technique. In contrast to some of the earlier approaches to modeling Lithium plating, the proposed model is able to predict non-trivial evolution of the concentrations in the relaxation regime when no current is applied to the cell. When equipped with an optimal constitutive relation, the model provides accurate predictions of the time evolution of both intercalated and plated Lithium across a wide range of charging/discharging rates. It can therefore serve as a useful tool for prediction and control of degradation mechanism in battery cells.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109252"},"PeriodicalIF":3.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A demand bidding model for multi-product industrial plants 多产品工业厂房需求投标模型

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-08-20 DOI: 10.1016/j.compchemeng.2025.109349

Xin Tang , Michael Baldea , Elaine T. Hale , Ross Baldick , Richard P. O’Neill

引用次数: 0

Deep learning adaptive Model Predictive Control of Fed-Batch Cultivations 饲料批量培养的深度学习自适应模型预测控制

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-08-20 DOI: 10.1016/j.compchemeng.2025.109344

Niels Krausch , Martin Doff-Sotta , Mark Cannon , Peter Neubauer , Mariano Nicolas Cruz Bournazou

{"title":"Deep learning adaptive Model Predictive Control of Fed-Batch Cultivations","authors":"Niels Krausch , Martin Doff-Sotta , Mark Cannon , Peter Neubauer , Mariano Nicolas Cruz Bournazou","doi":"10.1016/j.compchemeng.2025.109344","DOIUrl":"10.1016/j.compchemeng.2025.109344","url":null,"abstract":"<div><div>Bioprocesses are often characterized by nonlinear and uncertain dynamics, posing particular challenges for model predictive control (MPC) algorithms due to their computational demands when applied to nonlinear systems. Recent advances in optimal control theory have demonstrated that concepts from convex optimization, tube MPC, and differences of convex functions (DC) enable efficient, robust online process control. Our approach is based on DC decompositions of nonlinear dynamics and successive linearizations around predicted trajectories. By convexity, the linearization errors have tight bounds and can be treated as bounded disturbances within a robust tube MPC framework. We describe a systematic, data-driven method for computing DC model representations using deep neural networks with a special convex structure, and explain how the resulting MPC optimization can be solved using convex programming. For the problem of maximizing product formation in a cultivation with uncertain model parameters, we design a controller that ensures robust constraint satisfaction and allows online estimation of unknown model parameters. Our results indicate that this method is a promising solution for computationally tractable, robust MPC of bioprocesses.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109344"},"PeriodicalIF":3.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-based predictive control for pneumatic separation and classification of materials in lithium-ion battery recycling 基于模型的锂离子电池回收物料气动分离分类预测控制

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-08-19 DOI: 10.1016/j.compchemeng.2025.109358

Antonio I. García , Oscar A. Marín , Edelmira D. Gálvez

{"title":"Model-based predictive control for pneumatic separation and classification of materials in lithium-ion battery recycling","authors":"Antonio I. García , Oscar A. Marín , Edelmira D. Gálvez","doi":"10.1016/j.compchemeng.2025.109358","DOIUrl":"10.1016/j.compchemeng.2025.109358","url":null,"abstract":"<div><div>Due to the considerable number of lithium-ion batteries (LIBs) required for telecommunication systems, electric transport, and renewable energy storage, among other applications, the recycling of spent LIBs is considered an increasingly critical operation. The improvement of this operation can reduce manufacturing costs, the consumption of raw materials, and the environmental footprint produced by their disposal. The present work is focused on implementing advanced control strategies for the separation and classification stages in spent LIBs recycling. The control strategies used correspond to model-based predictive control (MPC). The methodology consisted of implementing a phenomenological model that represents the operation of a device that separates and classifies materials based on their physical properties and uses an air jet as a suspension media. The study presents five control scenarios simulated considering performance approaches and one scenario regarding economic approach. The two manipulated variables example obtained the highest relative error for the output variable concerning the set point, with 1.7125%. Implementing MPC controllers for the material separation stage in LIBs recycling would allow the improvement of these processes in both performance and economic aspects.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109358"},"PeriodicalIF":3.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0