Digital Chemical Engineering最新文献

{"title":"Applications of machine learning for modeling and advanced control of crystallization processes: Developments and perspectives","authors":"Fernando Arrais R.D. Lima ,&nbsp;Marcellus G.F. de Moraes ,&nbsp;Amaro G. Barreto Jr ,&nbsp;Argimiro R. Secchi ,&nbsp;Martha A. Grover ,&nbsp;Maurício B. de Souza Jr","doi":"10.1016/j.dche.2024.100208","DOIUrl":"10.1016/j.dche.2024.100208","url":null,"abstract":"<div><div>Crystallization is a separation method relevant to the production of medicines, food and many other products. An efficient crystallization process must obtain a product with the desired size, length, and purity. Therefore, models and control schemes are applied to achieve this goal. Artificial intelligence techniques, such as machine learning (ML), are applied for modeling and controlling these processes. The current review aims to present the use of ML for modeling and advanced control of crystallization processes. Considering modeling crystallization processes, this paper presents the advances and different uses of ML, such as neural networks, symbolic regression, and transformer algorithms. This review also presents the development of hybrid models combining ML with physical laws for crystallization processes. For the advanced control of crystallization processes, this review presents the development of advanced control strategies based on ML approaches, such as applying neural networks in a nonlinear model predictive controller and based on reinforcement learning. This work can be a relevant reference for the progress of the application of ML in the process systems engineering (PSE) to crystallization processes. It is also expected to encourage industry and academy to use these approaches for different crystallization processes.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100208"},"PeriodicalIF":3.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Catalytic pyrolysis of HDPE for enhanced hydrocarbon yield: A boosted regression tree assisted kinetics study for effective recycling of waste plastic","authors":"Shahina Riaz ,&nbsp;Nabeel Ahmad ,&nbsp;Wasif Farooq ,&nbsp;Imtiaz Ali ,&nbsp;Mohd Sajid ,&nbsp;Muhammad Naseem Akhtar","doi":"10.1016/j.dche.2024.100213","DOIUrl":"10.1016/j.dche.2024.100213","url":null,"abstract":"<div><div>Kinetic study is crucial in digital chemical engineering as a foundation for understanding and optimizing chemical processes. By analyzing reaction rates and mechanisms, kinetic models provide essential data for designing reactors, scaling processes, and predicting performance under various conditions. This study is part of a broader research series focused on efficiently converting waste plastics into hydrocarbons through catalytic pyrolysis. As the first study from the series, it investigates the thermal degradation of pristine high-density polyethylene (HDPE), aiming to understand its reaction kinetics under catalytic and non-catalytic conditions. The research employs iso-conventional methods to estimate the activation of energy HDPE and leverages a machine learning algorithm, specifically the BRT model, to effectively predict activation energy and optimize pyrolysis parameters. The activation energy (323 kJ/mol) of non-catalytic pyrolysis of HDPE was reduced to 164 kJ/mol during catalytic pyrolysis. Thermodynamic parameters such as change in activation enthalpy (<span><math><mrow><mstyle><mi>Δ</mi></mstyle><msup><mrow><mi>H</mi></mrow><mo>‡</mo></msup></mrow></math></span>), activation Gibbs free energy (<span><math><mrow><mstyle><mi>Δ</mi></mstyle><msup><mrow><mi>G</mi></mrow><mo>‡</mo></msup></mrow></math></span>) and, activation entropy (<span><math><mrow><mstyle><mi>Δ</mi></mstyle><msup><mrow><mi>S</mi></mrow><mo>‡</mo></msup></mrow></math></span>) were also significantly reduced during catalytic reaction. The statistical and machine-learning approaches were used in the kinetic analyses. Boosted regression trees (BRT) were used to predict the<span><math><mrow><mspace></mspace><msub><mi>E</mi><mi>a</mi></msub></mrow></math></span> during conversion at different heating rates for non-catalytic and catalytic processes. The liquid and gas fractions obtained from HDPE at different temperatures were characterized. The increase in yield of hydrocarbons at elevated temperatures indicated the reuse potential of plastic waste. The comprehensive analysis of HDPE exhibited 86 % of carbon and 14 % of hydrogen contributing to high heating value (HHV) of 44.41 MJ/kg.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100213"},"PeriodicalIF":3.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection","authors":"Eslam G. Al-Sakkari ,&nbsp;Ahmed Ragab ,&nbsp;Mostafa Amer ,&nbsp;Olumoye Ajao ,&nbsp;Marzouk Benali ,&nbsp;Daria C. Boffito ,&nbsp;Hanane Dagdougui ,&nbsp;Mouloud Amazouz","doi":"10.1016/j.dche.2024.100207","DOIUrl":"10.1016/j.dche.2024.100207","url":null,"abstract":"<div><div>Several processes and strategies have been developed to promote the utilization of lignin and to facilitate its market adoption across a broad spectrum of applications within the expanding lignin bioeconomy. However, the inherent variability in lignin properties, resulting from diverse feedstock sources and varied recovery and downstream processing methods, remains a significant challenge. This highlights the critical need to investigate lignin's miscibility and reactivity with polymers and solvents, as most lignin valorization pathways involve mixing, blending, or solubilization. Accurate estimation of Hansen solubility parameters (HSP) is crucial for solvent selection in several fields such as polymer science, coatings, adhesives, lignin-based biorefineries and solvent-based carbon capture. Traditional methods for predicting HSP are time-consuming and involve complex experiments, especially in applications dealing with carbon dioxide and lignin solubility. This paper introduces a novel ensemble modeling methodology based on machine learning (ML) techniques for accurate HSP prediction using Simplified Molecular Input Line Entry System (SMILES) codes as entries. The methodology integrates different ML approaches, including deep and shallow learning, to enhance prediction accuracy. Decision fusion of individual ML models is achieved through a hybrid approach combining non-learnable and learnable methods, resulting in reduced errors and enhanced accuracy. The results highlight the effectiveness of the ensemble-based methodology, which achieved 99% accuracy in predicting dispersion solubility parameters, outperforming other individual ML techniques. The proposed generic methodology, from data preprocessing to decision fusion through diverse ML algorithms, can be applied to various chemical analytics beyond HSP prediction.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100207"},"PeriodicalIF":3.0,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic and sustainability evaluation of green CO2-assisted propane dehydrogenation design","authors":"Guilherme V. Espinosa,&nbsp;Amanda L.T. Brandão","doi":"10.1016/j.dche.2024.100203","DOIUrl":"10.1016/j.dche.2024.100203","url":null,"abstract":"<div><div>Oxidative dehydrogenation of propane using CO₂ (ODPC) is among the most investigated on-purpose processes to meet the increased propylene demand, due to the necessity to reduce CO₂ emissions. In this context, the present work simulated an ODPC reactor integrated with chemical looping combustion (CLC) of biogas, which provides the necessary heat, and CO₂ capture technology in Aspen Plus. The simulation was evaluated based on economic and sustainability criteria. In addition, a kinetic model was proposed and validated for a sufficient range of operation. It was possible to achieve net present value (NPV) of -14.86 10⁶ US$, over a 15-year operational period, based on current carbon pricing policies. However, the potential profitability of the process was demonstrated by investigating the effects of more favorable carbon credit policies, with an increase from 50 to 120 US$ tCO₂eq^-1 resulting in a NPV of 164.15 10⁶ US$ and 4 years payback period.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100203"},"PeriodicalIF":3.0,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of on-line machine learning-based endpoint control and run-to-run control for an atomic layer etching process","authors":"Henrik Wang ,&nbsp;Feiyang Ou ,&nbsp;Julius Suherman ,&nbsp;Gerassimos Orkoulas ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2024.100206","DOIUrl":"10.1016/j.dche.2024.100206","url":null,"abstract":"<div><div>Control methods for Atomic Layer Etching (ALE) processes are constantly evolving due to the increasing level of precision needed to manufacture next-gen semiconductor devices. This work presents a novel, real-time Endpoint-based (EP) control approach for an Al₂O₃ ALE process in a discrete feed reactor. The proposed method dynamically adjusts the process time of both ALE half-cycles to ensure an optimal process outcome. The EP controller uses a machine learning-based transformer to take in variable-length, time-series pressure profiles to identify when the ALE process is complete. However, this model requires a large amount of process data to ensure that it will perform well even when under a variety of kinetic and pressure disturbances that mimic common issues in a real-world manufacturing environment. Thus, this work uses a multiscale modeling method that integrates a macroscopic Computational Fluid Dynamics (CFD) and a mesoscopic kinetic Monte Carlo (kMC) simulation to generate process data and test the proposed controllers. After testing the performance of the EP controller on individual runs, various combinations of ex-situ Run-to-Run (R2R) and EP controllers are examined in order to determine the strongest control strategy in a manufacturing environment. The final results show that the EP controller is highly accurate when trained on conditions that are representative of its implementation environment. Compared to traditional EWMA controllers, it has significantly fewer misprocesses, which enhances the overall control performance and efficiency of the ALE process.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100206"},"PeriodicalIF":3.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Process modelling and optimization of hydrogen production from biogas by integrating DWSIM with response surface methodology","authors":"Kaleem Ullah ,&nbsp;Sara Maen Asaad ,&nbsp;Abrar Inayat","doi":"10.1016/j.dche.2024.100205","DOIUrl":"10.1016/j.dche.2024.100205","url":null,"abstract":"<div><div>Hydrogen production from biogas presents a significant opportunity to address major sustainability challenges by providing an economically viable replacement of fossil fuels and reducing greenhouse gas emissions. However, the conversion of biogas into hydrogen using steam reforming is affected by several process parameters. Therefore, this study aims to use a combined approach of DWSIM chemical process simulator and response surface methodology (RSM) as an optimization technique to enhance the effectiveness of the hydrogen production process. The process was modeled with the help of DWSIM software and then validated. Additionally, sensitivity analysis was performed to assess the impact of varying raw material flow rates and reactor conditions on the hydrogen yield as well as investigate the effect of varying biogas composition on the hydrogen yield. Design Expert software was used to optimize the hydrogen production using the Central composite design and a quadratic model. Four input parameters were considered: biogas flow rate, steam flow rate, inlet temperature, and pressure of reformer reactor, with hydrogen yield at the outlet of the last reactor considered as the response. The model and the independent parameters were found to be significant with p-values&lt; 0.0001. The interactions of parameters showed that pressure had the least impact on the hydrogen yield. The optimal parameters identified were 57 kg/hr biogas flow rate, 33.97 kg/hr steam flow rate, 954.38 °C reformer inlet temperature, and 12.52 bar pressure, ultimately achieving a maximum hydrogen yield of 65.992 %. Validation of optimal conditions in DWSIM simulation tool yielded a hydrogen yield of 64.874 % with an error margin of &lt;2.0 %. Overall, this study demonstrates the effect of each parameter and optimizes the hydrogen production process to increase the yield.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100205"},"PeriodicalIF":3.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conversion of Spirulina platensis into methanol via gasification: Process simulation modeling and economic evaluation","authors":"Muhammad Shahbaz ,&nbsp;Muhammad Ammar ,&nbsp;Sukarni Sukarni","doi":"10.1016/j.dche.2024.100204","DOIUrl":"10.1016/j.dche.2024.100204","url":null,"abstract":"<div><div>The conversion of bioresources like Spirulina platensis (SP) into value-added chemicals, such as methanol, offers a sustainable replacement of fossil fuels and contributes to greenhouse gas mitigation. This study presents an integrated process simulation model, developed using Aspen Plus v10®, for the steam gasification of SP and subsequent methanol production. Process parameters, including temperature range from 650-950 °C, steam/feed ratio from 0.5–2, and recycle ratio from 0–9, were investigated to optimize syngas composition and methanol yield. Results demonstrated that increasing temperature enhances H₂ and CO production while reducing CO₂ and CH₄, significantly increasing methanol production from 6500 to 9500 kg/h. The steam/feed ratio also influences syngas composition and methanol yield, with higher ratios promoting H₂ and CO₂ production and reducing CO and CH₄. The economic evaluation of two scenarios, a base case and an optimum case, shows that the capital expenditure (Capex) and operating expenditure (Opex) are 19.3M$ and 9.07M$ for the base case, and 20.018M$ and 10.21M$ for the optimum case. The analysis also reveals that the optimum case, with higher methanol production (7.2 tonnes/h compared to 6.7 tonnes/h in the base case), generates a higher net income (9.76 M$/y) and reduces CO₂ emissions (4.918 tonnes CO₂-e/y compared to 5.72 tonnes CO₂-e/y). The energy flow indicates the input energy requirement, the energy carried by methanol, and the surplus energy, totalling 26740 kW to meet the major system's energy demands. This study provides valuable insights for researchers, policymakers, and commercial entities seeking to develop sustainable and economically viable biofuel production processes.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100204"},"PeriodicalIF":3.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Techno-economic analysis and process simulation of alkoxylated surfactant production in a circular carbon economy framework","authors":"Oliver J. Fisher ,&nbsp;Jhuma Sadhukhan ,&nbsp;Thorin Daniel ,&nbsp;Jin Xuan","doi":"10.1016/j.dche.2024.100199","DOIUrl":"10.1016/j.dche.2024.100199","url":null,"abstract":"<div><div>Successfully transitioning to a net-zero and circular carbon economy requires adopting innovative technologies and business models to capture CO₂ and convert it into valuable chemicals and materials. Given the high economic costs and limited funding available for this transition, robust economic modelling of potential circular carbon pathways is essential to identify economically viable routes. This study introduces a novel techno-economic analysis (TEA) of producing alcohol ethoxylate (AE7), a valuable surfactant, from industrial flue gas. Traditionally, AE7 is produced by reacting fatty alcohols with ethylene oxide derived from fossil or bio-based sources. This research explores a method using CO₂ captured from steel industry flue gas to produce AE7, addressing a notable gap in the literature. It evaluates a thermo-catalytic pathway involving Fischer-Tropsch (FT) synthesis with syngas generated by the reverse-water gas-shift reaction, where CO₂ reacts with H₂. CO₂ conversion rates range around 3% across processing capacities of 25 kt/a, 100 kt/a, and 1000 kt/a. The study finds that the CO₂ mass fraction concentration in the process emission is 2.47 × 10^–5, compared to 0.13 in the incoming flue gas, highlighting the system's positive environmental impact. A radial basis function neural network was built to forecast the long-term average price of fossil-based and bio-based surfactants to benchmark the results against. Economic analysis reveals that the cost of green hydrogen significantly impacts the minimum selling price (MSP), making cost parity with existing fossil-based surfactants challenging. The lowest MSP of $8.77/kg remains above the long-term forecasted price of $3.75/kg for fossil-based C_12–14 AE7. However, Monte Carlo simulations show a 21% probability of achieving a positive net present value (NPV) compared to leading bio-based surfactant alternatives. Sensitivity analyses identify capital costs, the price of low-carbon hydrogen (LCOH), and diesel prices as the most influential factors affecting the MSP. Continued advancements in Fischer-Tropsch catalyst technologies, reductions in green hydrogen costs and growing consumer demand for environmentally friendly products could significantly enhance the economic feasibility of this sustainable approach, paving the way for broader adoption and contributing to a circular carbon economy.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100199"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TorchSISSO: A PyTorch-based implementation of the sure independence screening and sparsifying operator for efficient and interpretable model discovery","authors":"Madhav Muthyala,&nbsp;Farshud Sorourifar,&nbsp;Joel A. Paulson","doi":"10.1016/j.dche.2024.100198","DOIUrl":"10.1016/j.dche.2024.100198","url":null,"abstract":"<div><div>Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact representations of complex data. Unlike traditional regression methods, SR explores progressively complex feature spaces, which can uncover simple models that generalize well, even from small datasets. Among SR algorithms, the Sure Independence Screening and Sparsifying Operator (SISSO) has proven particularly effective in the natural sciences, helping to rediscover fundamental physical laws as well as discover new interpretable equations for materials property modeling. However, its widespread adoption has been limited by performance inefficiencies and the challenges posed by its FORTRAN-based implementation, especially in modern computing environments. In this work, we introduce TorchSISSO, a native Python implementation built in the PyTorch framework. TorchSISSO leverages GPU acceleration, easy integration, and extensibility, offering a significant speed-up and improved accuracy over the original. We demonstrate that TorchSISSO matches or exceeds the performance of the original SISSO across a range of tasks, while dramatically reducing computational time and improving accessibility for broader scientific applications.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100198"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A nationwide planning model for argon supply chains with coordinated production and distribution","authors":"Sergio M.S. Neiro ,&nbsp;Tarun Madan ,&nbsp;Christos T. Maravelias ,&nbsp;José M. Pinto","doi":"10.1016/j.dche.2024.100201","DOIUrl":"10.1016/j.dche.2024.100201","url":null,"abstract":"<div><div>In this work, we address a nationwide tactical planning for industrial gas supply chains, particularly argon. The proposed approaches follow as extensions of our previous work (<em>Comp. &amp; Chem. Eng., 161 (2022) 107778</em>) in which a regional argon supply chain problem is addressed; in that work, both production and distribution could be represented in detail. Two different types of deliveries from the Air Separating Units (ASU) to customers, which involve single driver deliveries for short distance trips and sleeper team that require multiple days. The nationwide problem requires simplifications to keep the problem mathematically tractable, primarily the representation of production sites with different tier costs and the aggregation of customers in clusters. The regional problem addressed in our previous work is used as a benchmark case study for benchmarking. We then focus on a real-world problem that represents a nationwide argon supply chain. Despite the size of the models, near optimal solutions could be found in reasonable times. Finally, we highlight important features of the proposed approaches.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100201"},"PeriodicalIF":3.0,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}