Vianey de J. Cervantes-Güicho, Ana G. Reyes, Alberto Nuncio, Leonardo Sepúlveda-Torre, Cristina Landa-Cansigno, José A. Rodríguez-De la Garza, Miguel A. Medina-Morales, Leopoldo J. Ríos-González, Thelma K. Morales-Martínez
The guishe is a by-product of the fiber extraction from Agave lechuguilla. This material has no commercial value, although it contains metabolites that could be used as a resource for producing high-value products. This study optimized the DPPH• (2,2-diphenyl-1-picrylhydrazyl) antioxidant activity through microwave-assisted extraction (MAE) of polyphenolic compounds from Agave lechuguilla residues. The MAE process was optimized using a Box-Behnken design, with extraction time (5–15 min), temperature (40–50 °C), and solvent: sample ratio (1:20–1:30 m/v) as independent variables. In contrast, the dependent variable was DPPH• free radical scavenging activity. As a result, the highest antioxidant activity was at 8 min of irradiation, extraction temperature of 45 °C, and solvent: sample ratio 1:30 w/v, obtaining a total flavonoid content of 19.25 ± 0.60 mg QE/g DW, a total polyphenol content of 6.59 ± 0.31 mg GAE/g DW, a DPPH• free radical scavenging activity of 73.35 ± 1.90%, and an ABTS+• ([2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonate)]) free radical scavenging activity of 91.93 ± 0.68%.
{"title":"Box-Behnken Design for DPPH Free Radical Scavenging Activity Optimization from Microwave-Assisted Extraction of Polyphenolic Compounds from Agave lechuguilla Torr. Residues","authors":"Vianey de J. Cervantes-Güicho, Ana G. Reyes, Alberto Nuncio, Leonardo Sepúlveda-Torre, Cristina Landa-Cansigno, José A. Rodríguez-De la Garza, Miguel A. Medina-Morales, Leopoldo J. Ríos-González, Thelma K. Morales-Martínez","doi":"10.3390/pr12092005","DOIUrl":"https://doi.org/10.3390/pr12092005","url":null,"abstract":"The guishe is a by-product of the fiber extraction from Agave lechuguilla. This material has no commercial value, although it contains metabolites that could be used as a resource for producing high-value products. This study optimized the DPPH• (2,2-diphenyl-1-picrylhydrazyl) antioxidant activity through microwave-assisted extraction (MAE) of polyphenolic compounds from Agave lechuguilla residues. The MAE process was optimized using a Box-Behnken design, with extraction time (5–15 min), temperature (40–50 °C), and solvent: sample ratio (1:20–1:30 m/v) as independent variables. In contrast, the dependent variable was DPPH• free radical scavenging activity. As a result, the highest antioxidant activity was at 8 min of irradiation, extraction temperature of 45 °C, and solvent: sample ratio 1:30 w/v, obtaining a total flavonoid content of 19.25 ± 0.60 mg QE/g DW, a total polyphenol content of 6.59 ± 0.31 mg GAE/g DW, a DPPH• free radical scavenging activity of 73.35 ± 1.90%, and an ABTS+• ([2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonate)]) free radical scavenging activity of 91.93 ± 0.68%.","PeriodicalId":20597,"journal":{"name":"Processes","volume":"31 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Water can cause the growth and oxidation of Cu nanoparticles on the surface of Cu-based catalysts, leading to their deactivation. However, during methanol synthesis process from syngas on Cu-based catalysts, water is inevitably produced as a by-product due to the presence of CO2. Therefore, enhancing the stability of Cu-based catalysts during the reaction, particularly in the presence of water, is crucial. In this study, Cu/ZnO/Al2O3 was first subjected to wet etching and then hydrophobically modified using the sol–gel method with methyltrimethoxysilane (MTMS) and the grafting method with 1H,1H,2H,2H-perfluoroalkyltriethoxysilanes (PFOTES) as modifiers. These modifications aimed to mitigate the impact of water on the catalyst and improve its stability. After modification, the catalysts exhibited excellent hydrophobicity and enhanced catalytic activity in the methanol synthesis process. The surface physical properties, composition, and thermal stability of the catalysts before and after hydrophobic modification were characterized by SEM, FT-IR, BET, XRD and TGA. Additionally, molecular dynamics simulations were employed to compare the diffusion behavior of water molecules on the catalyst surfaces before and after hydrophobic modification. The results indicated that the modified catalyst surface formed a micro/nano structure composed of nanosheets and nanosheet clusters, while the hydrophobic modification did not alter the structure of the catalyst. According to the results of simulations, the hydrophobic layers on the modified catalysts were able to expel water quickly from the surfaces and reduce the relative concentration of water molecules at the active sites, thereby improving the stability of the catalyst. Notably, the thermal stability and hydrophobicity of the PFOTES-modified catalyst were superior to those of the MTMS-modified catalyst, resulting in a more significant enhancement in catalyst stability, which aligned with the experimental results.
{"title":"Thermal Stability Improvement of Cu-Based Catalyst by Hydrophobic Modification in Methanol Synthesis","authors":"Futao Ma, Jingjing Liu, Kaixuan Chen, Zhenmin Cheng","doi":"10.3390/pr12092008","DOIUrl":"https://doi.org/10.3390/pr12092008","url":null,"abstract":"Water can cause the growth and oxidation of Cu nanoparticles on the surface of Cu-based catalysts, leading to their deactivation. However, during methanol synthesis process from syngas on Cu-based catalysts, water is inevitably produced as a by-product due to the presence of CO2. Therefore, enhancing the stability of Cu-based catalysts during the reaction, particularly in the presence of water, is crucial. In this study, Cu/ZnO/Al2O3 was first subjected to wet etching and then hydrophobically modified using the sol–gel method with methyltrimethoxysilane (MTMS) and the grafting method with 1H,1H,2H,2H-perfluoroalkyltriethoxysilanes (PFOTES) as modifiers. These modifications aimed to mitigate the impact of water on the catalyst and improve its stability. After modification, the catalysts exhibited excellent hydrophobicity and enhanced catalytic activity in the methanol synthesis process. The surface physical properties, composition, and thermal stability of the catalysts before and after hydrophobic modification were characterized by SEM, FT-IR, BET, XRD and TGA. Additionally, molecular dynamics simulations were employed to compare the diffusion behavior of water molecules on the catalyst surfaces before and after hydrophobic modification. The results indicated that the modified catalyst surface formed a micro/nano structure composed of nanosheets and nanosheet clusters, while the hydrophobic modification did not alter the structure of the catalyst. According to the results of simulations, the hydrophobic layers on the modified catalysts were able to expel water quickly from the surfaces and reduce the relative concentration of water molecules at the active sites, thereby improving the stability of the catalyst. Notably, the thermal stability and hydrophobicity of the PFOTES-modified catalyst were superior to those of the MTMS-modified catalyst, resulting in a more significant enhancement in catalyst stability, which aligned with the experimental results.","PeriodicalId":20597,"journal":{"name":"Processes","volume":"4 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oil sludge is one of the main pollutants generated by the oil industry. Due to serious pollution and increasing oil production, problems arise every year in the effective treatment of oil sludge. The current study examines the composition and physicochemical characteristics of oil sludge, as well as traditional and new methods for processing oil sludge. With the tightening of environmental protection requirements, oil sludge quality reduction, recycling, and harmless treatment technologies will become necessary in the future. The primary task was to determine the composition of tank oil sludge, separate it from mechanical impurities, and study the influence of ultrasonic treatment and subsequent atmospheric distillation on the extract. The separation of the concentrate and the composition of the tank oil sludge, using an extracted mixture of hexane and benzene, are considered. The use of modern SEM methods, elemental analysis, NMR analysis, IR, ultrasound, and GC–mass spectrometry made it possible to characterize the organic part of reservoir oil sludge and its distillation products. First, 300 g of tank oil sludge was preheated and mixed with 300 mL of solvent (hexane:benzene = 1:1). After mixing with the solvent, the result mixture was filtered. Then, it was placed in an ultrasonic bath and exposed to ultrasound at a frequency of 100 kHz for 30 min. After processing, it was extracted in a Soxhlet apparatus at a temperature of 65 °C to isolate the extract. The resulting extract was analyzed on a gas chromatograph with mass detection. The composition of the extract was as follows (in %): hexane—83.99; total hydrocarbon isomers—7.12; n-hydrocarbons—2.52; benzene—6.37%. At a temperature of 85 °C, the benzene yield was 65.85%. It has been established that the fractions obtained through the distillation of oil sludge at temperatures of 65–85 °C have improved dissolving capacity. It has also been shown that the use of these fractions promotes an increase in the content of hydrocarbon isomers by 12–13% in the extract composition.
油泥是石油工业产生的主要污染物之一。由于污染严重和石油产量不断增加，每年都会出现如何有效处理油污泥的问题。本研究探讨了油污泥的组成和理化特性，以及处理油污泥的传统方法和新方法。随着环保要求的提高，油污泥的减质、回收和无害化处理技术将成为未来的必要技术。主要任务是确定油罐油泥的成分，将其从机械杂质中分离出来，并研究超声波处理和随后的常压蒸馏对提取物的影响。使用正己烷和苯的萃取混合物，考虑了浓缩物的分离和油罐油泥的成分。通过使用现代扫描电镜方法、元素分析、核磁共振分析、红外光谱、超声波和气相色谱-质谱法，可以确定油库油泥的有机部分及其蒸馏产物的特征。首先，将 300 克油库油泥预热并与 300 毫升溶剂（正己烷：苯 = 1:1）混合。与溶剂混合后，过滤所得混合物。然后，将其放入超声波浴中，在频率为 100 kHz 的超声波下暴露 30 分钟。处理后，在温度为 65 °C 的索氏提取器中进行提取，分离出提取物。所得提取物在气相色谱仪上进行质量检测分析。提取物的成分如下（单位：%）：正己烷-83.99；总烃异构体-7.12；正烃-2.52；苯-6.37%。在 85 °C 的温度下，苯的产率为 65.85%。实验证明，在 65-85 °C 的温度下蒸馏油泥得到的馏分具有更好的溶解能力。研究还表明，使用这些馏分可使萃取物成分中碳氢化合物异构体的含量增加 12-13%。
{"title":"Studying the Characteristics of Tank Oil Sludge","authors":"Sandugash Tanirbergenova, Aisulu Tagayeva, Cesare Oliviero Rossi, Michele Porto, Paolino Caputo, Ernar Kanzharkan, Dildara Tugelbayeva, Nurzhamal Zhylybayeva, Kairat Tazhu, Yerbol Tileuberdi","doi":"10.3390/pr12092007","DOIUrl":"https://doi.org/10.3390/pr12092007","url":null,"abstract":"Oil sludge is one of the main pollutants generated by the oil industry. Due to serious pollution and increasing oil production, problems arise every year in the effective treatment of oil sludge. The current study examines the composition and physicochemical characteristics of oil sludge, as well as traditional and new methods for processing oil sludge. With the tightening of environmental protection requirements, oil sludge quality reduction, recycling, and harmless treatment technologies will become necessary in the future. The primary task was to determine the composition of tank oil sludge, separate it from mechanical impurities, and study the influence of ultrasonic treatment and subsequent atmospheric distillation on the extract. The separation of the concentrate and the composition of the tank oil sludge, using an extracted mixture of hexane and benzene, are considered. The use of modern SEM methods, elemental analysis, NMR analysis, IR, ultrasound, and GC–mass spectrometry made it possible to characterize the organic part of reservoir oil sludge and its distillation products. First, 300 g of tank oil sludge was preheated and mixed with 300 mL of solvent (hexane:benzene = 1:1). After mixing with the solvent, the result mixture was filtered. Then, it was placed in an ultrasonic bath and exposed to ultrasound at a frequency of 100 kHz for 30 min. After processing, it was extracted in a Soxhlet apparatus at a temperature of 65 °C to isolate the extract. The resulting extract was analyzed on a gas chromatograph with mass detection. The composition of the extract was as follows (in %): hexane—83.99; total hydrocarbon isomers—7.12; n-hydrocarbons—2.52; benzene—6.37%. At a temperature of 85 °C, the benzene yield was 65.85%. It has been established that the fractions obtained through the distillation of oil sludge at temperatures of 65–85 °C have improved dissolving capacity. It has also been shown that the use of these fractions promotes an increase in the content of hydrocarbon isomers by 12–13% in the extract composition.","PeriodicalId":20597,"journal":{"name":"Processes","volume":"10 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yihong Li, Ligang Tang, Lei Yao, Bo Gao, Xue Yuan, Changsheng Shi
The flotation effect of lean coal is crucial for its clean utilization. Therefore, the flotation characteristics of difficult-to-float lean coal were studied. The analysis results of the feed properties showed that the ash content of the feed was high and the particle size was very fine. The minerals in the gangue mainly included sericite, kaolinite, quartz, white mica, and other substances. After flotation, the functional groups of the coal particles in the tailings decreased, and the absorption peak intensity weakened. Furthermore, the results of multi-factor flotation experiments showed that the dosages of the collector and the frother were significant factors affecting the yield of clean coal. The clean coal yield gradually increased with an increase in the two factors. The ash content of the clean coal increased with an increase in the frother dosage. Within the range of feed concentrations used in this work, the feed concentration was not a significant factor affecting the clean coal’s yield and ash content. Prediction models for the clean coal yield and ash content were proposed. Under optimized experimental conditions, the clean coal yield and the flotation perfection index were 72.15% and 46.63%, respectively, indicating a good flotation effect.
{"title":"Particle Properties and Flotation Characteristics of Difficult-to-Float Lean Coal","authors":"Yihong Li, Ligang Tang, Lei Yao, Bo Gao, Xue Yuan, Changsheng Shi","doi":"10.3390/pr12092004","DOIUrl":"https://doi.org/10.3390/pr12092004","url":null,"abstract":"The flotation effect of lean coal is crucial for its clean utilization. Therefore, the flotation characteristics of difficult-to-float lean coal were studied. The analysis results of the feed properties showed that the ash content of the feed was high and the particle size was very fine. The minerals in the gangue mainly included sericite, kaolinite, quartz, white mica, and other substances. After flotation, the functional groups of the coal particles in the tailings decreased, and the absorption peak intensity weakened. Furthermore, the results of multi-factor flotation experiments showed that the dosages of the collector and the frother were significant factors affecting the yield of clean coal. The clean coal yield gradually increased with an increase in the two factors. The ash content of the clean coal increased with an increase in the frother dosage. Within the range of feed concentrations used in this work, the feed concentration was not a significant factor affecting the clean coal’s yield and ash content. Prediction models for the clean coal yield and ash content were proposed. Under optimized experimental conditions, the clean coal yield and the flotation perfection index were 72.15% and 46.63%, respectively, indicating a good flotation effect.","PeriodicalId":20597,"journal":{"name":"Processes","volume":"15 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei He, Benshun Chen, Bin Yin, Jianren Ye, Yucai He
Simvastatin is a widely used statin medication that is commonly prescribed to lower cholesterol levels and reduce the risk of cardiovascular events. It is marketed under the brand name Zocor and is known for its effectiveness in treating high cholesterol and managing cardiovascular disease. Monacolin J is an important precursor used to synthesize simvastatin and is mainly produced by chemical methods in industry. Here, monacolin J was synthesized through an enzymatic method under optimized reaction conditions. One recombinant Escherichia coli BL21 (DE3) strain containing lovastatin hydrolase (encoded by CDV55_102090) from Aspergillus turcosus was constructed, which effectively transformed 100 g/L of lovastatin to monacolin J within 3.5 h at pH 8.0 and 30 °C, with a conversion rate of >99.8%. Furthermore, the T5010, the temperature at which the residual activity was half of the initial enzymatic activity after 10 min of heat treatment, was >50 °C, indicating the tremendous potential of this bioprocess for synthesizing monacolin J at an industrial scale.
{"title":"Efficient Biosynthesis of Monacolin J through Enzymatic Hydrolysis Using a Recombinant Lovastatin Hydrolase","authors":"Wei He, Benshun Chen, Bin Yin, Jianren Ye, Yucai He","doi":"10.3390/pr12092006","DOIUrl":"https://doi.org/10.3390/pr12092006","url":null,"abstract":"Simvastatin is a widely used statin medication that is commonly prescribed to lower cholesterol levels and reduce the risk of cardiovascular events. It is marketed under the brand name Zocor and is known for its effectiveness in treating high cholesterol and managing cardiovascular disease. Monacolin J is an important precursor used to synthesize simvastatin and is mainly produced by chemical methods in industry. Here, monacolin J was synthesized through an enzymatic method under optimized reaction conditions. One recombinant Escherichia coli BL21 (DE3) strain containing lovastatin hydrolase (encoded by CDV55_102090) from Aspergillus turcosus was constructed, which effectively transformed 100 g/L of lovastatin to monacolin J within 3.5 h at pH 8.0 and 30 °C, with a conversion rate of >99.8%. Furthermore, the T5010, the temperature at which the residual activity was half of the initial enzymatic activity after 10 min of heat treatment, was >50 °C, indicating the tremendous potential of this bioprocess for synthesizing monacolin J at an industrial scale.","PeriodicalId":20597,"journal":{"name":"Processes","volume":"2 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The primary factor constraining the performance of unconsolidated sandstone reservoirs is blockage from particle migration, which reduces the capacity of liquid production. By utilizing logging, seismic, core−testing, and oil−well production data, the reservoir damage induced by particle migration in the Bohai A oilfield was characterized and predicted through combined well–seismic methods. This research highlights the porosity, permeability, median grain diameter, and pore structure as the primary parameters influencing reservoir characteristics. Based on their permeability differences, reservoirs can be categorized into Type I (permeability ≥ 800 mD), Type II (400 mD < permeability < 800 mD), and Type III (permeability ≤ 400 mD). The results of the core displacement experiments revealed that, compared to their initial states, the permeability change rates for Type I and Type II reservoirs exceeded 50%, whereas the permeability change rate for Type III reservoirs surpassed 200%. Furthermore, by combining this quantitative relationship model with machine learning techniques and well–seismic methods, the distribution of permeability change rates caused by particle migration across the entire region was successfully predicted and validated against production data from three oil wells. In addition, to build a reliable deep learning model, a sensitivity analysis of the hyperparameters was conducted to determine the activation function, optimizer, learning rate, and neurons. This method enhances the prediction efficiency of reservoir permeability changes in offshore oilfields with limited coring data, providing important decision support for reservoir protection and field development.
{"title":"Damage Evaluation of Unconsolidated Sandstone Particle Migration Reservoir Based on Well–Seismic Combination","authors":"Zhao Wang, Hanjun Yin, Haoxuan Tang, Yawei Hou, Hang Yu, Qiang Liu, Hongming Tang, Tianze Jia","doi":"10.3390/pr12092009","DOIUrl":"https://doi.org/10.3390/pr12092009","url":null,"abstract":"The primary factor constraining the performance of unconsolidated sandstone reservoirs is blockage from particle migration, which reduces the capacity of liquid production. By utilizing logging, seismic, core−testing, and oil−well production data, the reservoir damage induced by particle migration in the Bohai A oilfield was characterized and predicted through combined well–seismic methods. This research highlights the porosity, permeability, median grain diameter, and pore structure as the primary parameters influencing reservoir characteristics. Based on their permeability differences, reservoirs can be categorized into Type I (permeability ≥ 800 mD), Type II (400 mD < permeability < 800 mD), and Type III (permeability ≤ 400 mD). The results of the core displacement experiments revealed that, compared to their initial states, the permeability change rates for Type I and Type II reservoirs exceeded 50%, whereas the permeability change rate for Type III reservoirs surpassed 200%. Furthermore, by combining this quantitative relationship model with machine learning techniques and well–seismic methods, the distribution of permeability change rates caused by particle migration across the entire region was successfully predicted and validated against production data from three oil wells. In addition, to build a reliable deep learning model, a sensitivity analysis of the hyperparameters was conducted to determine the activation function, optimizer, learning rate, and neurons. This method enhances the prediction efficiency of reservoir permeability changes in offshore oilfields with limited coring data, providing important decision support for reservoir protection and field development.","PeriodicalId":20597,"journal":{"name":"Processes","volume":"4 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid increase in the number of vehicles worldwide, we are currently experiencing a scarcity of nonrenewable energy, such as fuel, and the resulting environmental risks associated with vehicle utilization [...]
{"title":"Optimization of Operating Conditions for Battery Thermal Management System","authors":"Yi-Long Lou, Kang Zhang, Zhen-Zhe Li","doi":"10.3390/pr12092003","DOIUrl":"https://doi.org/10.3390/pr12092003","url":null,"abstract":"With the rapid increase in the number of vehicles worldwide, we are currently experiencing a scarcity of nonrenewable energy, such as fuel, and the resulting environmental risks associated with vehicle utilization [...]","PeriodicalId":20597,"journal":{"name":"Processes","volume":"21 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Manuel Vargas, Rodolfo Mosquera, Guillermo Fuertes, Miguel Alfaro, Ileana Gloria Perez Vergara
This study offers an innovative solution to address performance issues in the manufacturing process of garlic salt within a condiment-producing SME. A hybrid Lean/Six Sigma model utilizing a Surface Tension Neural Network (STNN) was implemented to control temperature and relative humidity in real-time. The model follows the Define, Measure, Analyze, Improve, Control (DMAIC) methodology to identify root causes and correlate them with waste. By integrating statistical tools, artificial intelligence, and engineering design principles, alternative solutions were evaluated to minimize waste. This document contributes to existing knowledge by demonstrating the integration of an STNN with the Lean/Six Sigma framework in condiment production, an area with limited empirical research. It underscores the benefits of advanced AI technologies in enhancing traditional process optimization methods. The STNN model achieved 97.31% accuracy for temperature classification and 97.37% for humidity, outperforming a Naive Bayes model, which attained 90% accuracy for both. The results showed a 3.15% increase in yield, saving 39.7 kg of waste per batch. Additionally, a 2.13-point improvement at the Six Sigma level was achieved, reducing defects per million opportunities by 551.722. These improvements resulted in significant cost savings, with a reduction in waste-related losses amounting to USD 1585 per batch. The study demonstrates that incorporating artificial intelligence into the Lean/Six Sigma methodology effectively addresses the limitations of traditional statistical methods. Significant improvements in yield and waste reduction highlight the potential of this approach, enhancing operational efficiency and profitability, and fostering sustainable manufacturing practices critical for SMEs’ competitiveness and sustainability in the global market.
{"title":"Process Optimization in a Condiment SME through Improved Lean Six Sigma with a Surface Tension Neural Network","authors":"Manuel Vargas, Rodolfo Mosquera, Guillermo Fuertes, Miguel Alfaro, Ileana Gloria Perez Vergara","doi":"10.3390/pr12092001","DOIUrl":"https://doi.org/10.3390/pr12092001","url":null,"abstract":"This study offers an innovative solution to address performance issues in the manufacturing process of garlic salt within a condiment-producing SME. A hybrid Lean/Six Sigma model utilizing a Surface Tension Neural Network (STNN) was implemented to control temperature and relative humidity in real-time. The model follows the Define, Measure, Analyze, Improve, Control (DMAIC) methodology to identify root causes and correlate them with waste. By integrating statistical tools, artificial intelligence, and engineering design principles, alternative solutions were evaluated to minimize waste. This document contributes to existing knowledge by demonstrating the integration of an STNN with the Lean/Six Sigma framework in condiment production, an area with limited empirical research. It underscores the benefits of advanced AI technologies in enhancing traditional process optimization methods. The STNN model achieved 97.31% accuracy for temperature classification and 97.37% for humidity, outperforming a Naive Bayes model, which attained 90% accuracy for both. The results showed a 3.15% increase in yield, saving 39.7 kg of waste per batch. Additionally, a 2.13-point improvement at the Six Sigma level was achieved, reducing defects per million opportunities by 551.722. These improvements resulted in significant cost savings, with a reduction in waste-related losses amounting to USD 1585 per batch. The study demonstrates that incorporating artificial intelligence into the Lean/Six Sigma methodology effectively addresses the limitations of traditional statistical methods. Significant improvements in yield and waste reduction highlight the potential of this approach, enhancing operational efficiency and profitability, and fostering sustainable manufacturing practices critical for SMEs’ competitiveness and sustainability in the global market.","PeriodicalId":20597,"journal":{"name":"Processes","volume":"15 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiangwei Gao, Yunliang Yu, Zhongjie Xu, Yingchun Liu
Volcanic rock reservoirs for oil and gas are known worldwide for their considerable heterogeneity. Micropores and fractures play vital roles in the storage and transportation of natural gas. Samples from volcanic reservoirs in Songliao Basin, CS1 and W21, belonging to the Changling fault depression and the Wangfu fault depression, respectively, have similar lithology. This study employs micro–nano CT scanning technology to systematically identify the key parameters and transport capacities of natural gas within volcanic reservoirs. Using Avizo 2020.1software, a 3D digital representation of rock core was reconstructed to model pore distribution, connectivity, pore–throat networks, and fractures. These models are then analyzed to evaluate pore/throat structures and fractures alongside microscopic parameters. The relationship between micropore–throat structure parameters and permeability was investigated by microscale gas flow simulations and Pearson correlation analyses. The results showed that the CS1 sample significantly exceeded the W21 sample in terms of pore connectivity and permeability, with connected pore volume, throat count, and specific surface area being more than double that of the W21 sample. Pore–throat parameters are decisive for natural gas storage and transport. Additionally, based on seepage simulation and the pore–throat model, the specific influence of pore–throat structure parameters on permeability in volcanic reservoirs was quantified. In areas with well–developed fractures, gas seepage pathways mainly follow fractures, significantly improving gas flow efficiency. In areas with fewer fractures, throat radius has the most significant impact on permeability, followed by pore radius and throat length.
{"title":"Micro–Nano 3D CT Scanning to Assess the Impact of Microparameters of Volcanic Reservoirs on Gas Migration","authors":"Xiangwei Gao, Yunliang Yu, Zhongjie Xu, Yingchun Liu","doi":"10.3390/pr12092000","DOIUrl":"https://doi.org/10.3390/pr12092000","url":null,"abstract":"Volcanic rock reservoirs for oil and gas are known worldwide for their considerable heterogeneity. Micropores and fractures play vital roles in the storage and transportation of natural gas. Samples from volcanic reservoirs in Songliao Basin, CS1 and W21, belonging to the Changling fault depression and the Wangfu fault depression, respectively, have similar lithology. This study employs micro–nano CT scanning technology to systematically identify the key parameters and transport capacities of natural gas within volcanic reservoirs. Using Avizo 2020.1software, a 3D digital representation of rock core was reconstructed to model pore distribution, connectivity, pore–throat networks, and fractures. These models are then analyzed to evaluate pore/throat structures and fractures alongside microscopic parameters. The relationship between micropore–throat structure parameters and permeability was investigated by microscale gas flow simulations and Pearson correlation analyses. The results showed that the CS1 sample significantly exceeded the W21 sample in terms of pore connectivity and permeability, with connected pore volume, throat count, and specific surface area being more than double that of the W21 sample. Pore–throat parameters are decisive for natural gas storage and transport. Additionally, based on seepage simulation and the pore–throat model, the specific influence of pore–throat structure parameters on permeability in volcanic reservoirs was quantified. In areas with well–developed fractures, gas seepage pathways mainly follow fractures, significantly improving gas flow efficiency. In areas with fewer fractures, throat radius has the most significant impact on permeability, followed by pore radius and throat length.","PeriodicalId":20597,"journal":{"name":"Processes","volume":"10 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yonglin Guo, Di Zhou, Huimin Chen, Xiaoli Yue, Yuyu Cheng
The finishing mill is a critical link in the hot rolling process, influencing the final product’s quality, and even economic efficiency. The distribution box of the finishing mill plays a vital role in power transmission and distribution. However, harsh operating conditions can frequently lead to distribution box damage and even failure. To diagnose faults in the distribution box promptly, a fault diagnosis network model is constructed in this paper. This model combines depthwise separable convolution and Bi−LSTM. Depthwise separable convolution and Bi−LSTM can extract both spatial and temporal features from signals. This structure enables comprehensive feature extraction and fully utilizes signal information. To verify the diagnostic capability of the model, five types of data are collected and used: the pitting of tooth flank, flat−headed sleeve tooth crack, gear surface crack, gear tooth surface spalling, and normal conditions. The model achieves an accuracy of 97.46% and incorporates a lightweight design, which enhances computational efficiency. Furthermore, the model maintains approximately 90% accuracy under three noise conditions. Based on these results, the proposed model can effectively diagnose faults in the distribution box, and reduce downtime in engineering.
{"title":"Fault Intelligent Diagnosis for Distribution Box in Hot Rolling Based on Depthwise Separable Convolution and Bi−LSTM","authors":"Yonglin Guo, Di Zhou, Huimin Chen, Xiaoli Yue, Yuyu Cheng","doi":"10.3390/pr12091999","DOIUrl":"https://doi.org/10.3390/pr12091999","url":null,"abstract":"The finishing mill is a critical link in the hot rolling process, influencing the final product’s quality, and even economic efficiency. The distribution box of the finishing mill plays a vital role in power transmission and distribution. However, harsh operating conditions can frequently lead to distribution box damage and even failure. To diagnose faults in the distribution box promptly, a fault diagnosis network model is constructed in this paper. This model combines depthwise separable convolution and Bi−LSTM. Depthwise separable convolution and Bi−LSTM can extract both spatial and temporal features from signals. This structure enables comprehensive feature extraction and fully utilizes signal information. To verify the diagnostic capability of the model, five types of data are collected and used: the pitting of tooth flank, flat−headed sleeve tooth crack, gear surface crack, gear tooth surface spalling, and normal conditions. The model achieves an accuracy of 97.46% and incorporates a lightweight design, which enhances computational efficiency. Furthermore, the model maintains approximately 90% accuracy under three noise conditions. Based on these results, the proposed model can effectively diagnose faults in the distribution box, and reduce downtime in engineering.","PeriodicalId":20597,"journal":{"name":"Processes","volume":"15 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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