Pub Date : 2023-07-20DOI: 10.3389/fceng.2023.1182817
Naveen G. Jesubalan, Garima Thakur, A. Rathore
Single-pass tangential flow filtration (SPTFF) is a crucial technology enabling the continuous manufacturing of monoclonal antibodies (mAbs). By significantly increasing the membrane area utilized in the process, SPTFF allows the mAb process stream to be concentrated up to the desired final target in a single pass across the membrane surface without the need for recirculation. However, a key challenge in SPTFF is compensating for flux decline across the membrane due to concentration polarization and surface fouling phenomena. In continuous downstream processing, flux decline immediately impacts the continuous process flowrates. It reduces the concentration factor achievable in a single pass, thereby reducing the final concentration attained at the outlet of the SPTFF module. In this work, we develop a deep neural network model to predict the NWP in real-time without the need to conduct actual NWP measurements. The developed model incorporates process parameters such as pressure and feed concentrations through inline sensors and a spectroscopy-coupled data model (NIR-PLS model). The model determines the optimal timing for membrane cleaning steps when the normalized water permeability (NWP) falls below 60%. Using SCADA and PLC, a distributed control system was developed to integrate the monitoring sensors and control elements, such as the NIRS sensor for concentration monitoring, the DNN model for NWP prediction, weighing balances, pressure sensors, pumps, and valves. The model was tested in real-time, and the NWP was predicted within <5% error in three independent test cases, successfully enabling control of the SPTFF step in line with the Quality by Design paradigm.
{"title":"Deep neural network for prediction and control of permeability decline in single pass tangential flow ultrafiltration in continuous processing of monoclonal antibodies","authors":"Naveen G. Jesubalan, Garima Thakur, A. Rathore","doi":"10.3389/fceng.2023.1182817","DOIUrl":"https://doi.org/10.3389/fceng.2023.1182817","url":null,"abstract":"Single-pass tangential flow filtration (SPTFF) is a crucial technology enabling the continuous manufacturing of monoclonal antibodies (mAbs). By significantly increasing the membrane area utilized in the process, SPTFF allows the mAb process stream to be concentrated up to the desired final target in a single pass across the membrane surface without the need for recirculation. However, a key challenge in SPTFF is compensating for flux decline across the membrane due to concentration polarization and surface fouling phenomena. In continuous downstream processing, flux decline immediately impacts the continuous process flowrates. It reduces the concentration factor achievable in a single pass, thereby reducing the final concentration attained at the outlet of the SPTFF module. In this work, we develop a deep neural network model to predict the NWP in real-time without the need to conduct actual NWP measurements. The developed model incorporates process parameters such as pressure and feed concentrations through inline sensors and a spectroscopy-coupled data model (NIR-PLS model). The model determines the optimal timing for membrane cleaning steps when the normalized water permeability (NWP) falls below 60%. Using SCADA and PLC, a distributed control system was developed to integrate the monitoring sensors and control elements, such as the NIRS sensor for concentration monitoring, the DNN model for NWP prediction, weighing balances, pressure sensors, pumps, and valves. The model was tested in real-time, and the NWP was predicted within <5% error in three independent test cases, successfully enabling control of the SPTFF step in line with the Quality by Design paradigm.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41756806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-14DOI: 10.3389/fceng.2023.1237945
Muhammad Sajid, J.H.P. Américo-Pinheiro, Abdur Raheem, M. M. Azim
{"title":"Editorial: Advances in the sustainable production of biofuels and bioderivatives","authors":"Muhammad Sajid, J.H.P. Américo-Pinheiro, Abdur Raheem, M. M. Azim","doi":"10.3389/fceng.2023.1237945","DOIUrl":"https://doi.org/10.3389/fceng.2023.1237945","url":null,"abstract":"","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44476815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-12DOI: 10.3389/fceng.2023.1138283
Tianxing Cai, Jian Fang, Sharath Daida, H. Lou
The chemical process industry (CPI) accumulated a rich data asset through industrial 4.0. There is a strong drive to develop and utilize effective approaches for process performance prediction and improvement, process control, sensor development, asset management, etc. The synergy between machine learning and first principles models can bring new insights and add tremendous value to the CPI. This paper reviews various applications of the synergies towards asset integrity management. An overview of some related commercial software packages are also provided.
{"title":"Review of synergy between machine learning and first principles models for asset integrity management","authors":"Tianxing Cai, Jian Fang, Sharath Daida, H. Lou","doi":"10.3389/fceng.2023.1138283","DOIUrl":"https://doi.org/10.3389/fceng.2023.1138283","url":null,"abstract":"The chemical process industry (CPI) accumulated a rich data asset through industrial 4.0. There is a strong drive to develop and utilize effective approaches for process performance prediction and improvement, process control, sensor development, asset management, etc. The synergy between machine learning and first principles models can bring new insights and add tremendous value to the CPI. This paper reviews various applications of the synergies towards asset integrity management. An overview of some related commercial software packages are also provided.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45238061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-11DOI: 10.3389/fceng.2023.1193806
Asad Ali, Khurram Shahzad Ayub, Muhammad Tahseen Sadiq, M. Tanveer, Hamza Naseer, Zoha Nadeem, Hafiz Muhammad Aamir
In an agricultural country like Pakistan, producing affordable and clean energy can be a challenging task. However, Pakistan has the potential to utilize various biomass feedstocks to generate renewable energy and tackle climate change while promoting sustainable development. Wheat, rice, sugarcane, and corn are the four main crops that yield a significant amount of residue, totaling 112.1 million tons per year. These residues have the potential to produce 3,050 kWh/ton of energy, which can meet 14% of the energy demand in Pakistan, equivalent to 9.85TW, starting in 2022. Gasification technology is a versatile option that efficiently converts biomass into energy while reducing negative environmental impacts. The current research explores the feasibility of generating clean energy from crop residues with low emissions, addressing the country’s energy needs, and supporting policymakers in promoting the use of biomass for energy production. According to this study, rice husk, corn cobs, wheat straw, and sugar bagasse all produce hydrogen at rates of 6.9 wt.%, 6.4 wt.%, 5.69 wt.%, and 5.35 wt.%, respectively. Therefore, our study demonstrates that corn cobs have a significant potential for energy production.
{"title":"Potential and prospects of biomass as a source of renewable energy in Pakistan","authors":"Asad Ali, Khurram Shahzad Ayub, Muhammad Tahseen Sadiq, M. Tanveer, Hamza Naseer, Zoha Nadeem, Hafiz Muhammad Aamir","doi":"10.3389/fceng.2023.1193806","DOIUrl":"https://doi.org/10.3389/fceng.2023.1193806","url":null,"abstract":"In an agricultural country like Pakistan, producing affordable and clean energy can be a challenging task. However, Pakistan has the potential to utilize various biomass feedstocks to generate renewable energy and tackle climate change while promoting sustainable development. Wheat, rice, sugarcane, and corn are the four main crops that yield a significant amount of residue, totaling 112.1 million tons per year. These residues have the potential to produce 3,050 kWh/ton of energy, which can meet 14% of the energy demand in Pakistan, equivalent to 9.85TW, starting in 2022. Gasification technology is a versatile option that efficiently converts biomass into energy while reducing negative environmental impacts. The current research explores the feasibility of generating clean energy from crop residues with low emissions, addressing the country’s energy needs, and supporting policymakers in promoting the use of biomass for energy production. According to this study, rice husk, corn cobs, wheat straw, and sugar bagasse all produce hydrogen at rates of 6.9 wt.%, 6.4 wt.%, 5.69 wt.%, and 5.35 wt.%, respectively. Therefore, our study demonstrates that corn cobs have a significant potential for energy production.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44686117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.3389/fceng.2023.1175235
L. M. Carvalho, N. V. Silva, L. G. D. de Abreu, Marina Pupke Marone, Alexandra Russolo Cardelli, Fábio Trigo Raya, Guido Araujo, M. Carazzolle, G. G. Guimarães Pereira
Agave plants are well-known for their drought resilience and commercial applications. Among them, Agave sisalana (sisal) is the species most used to produce hard fibers, and it is of great importance for semiarid regions. Agaves also show potential as bioenergy feedstocks, as they can accumulate large amounts of biomass and fermentable sugar. This study aimed to reconstruct the A. sisalana interactome, and identify key genes and modules involved in multiple plant tissues (root, stem, and leaf) through RNA-Seq analysis. We integrated A. sisalana transcriptome sequences and gene expression generated from stem, leaf, and root tissues to build global and conditional co-expression networks across the entire transcriptome. By combining the co-expression network, module classification, and function enrichment tools, we identified 20 functional modules related to at least one A. sisalana tissue, covering functions such as photosynthesis, leaf formation, auxin-activated signaling pathway, floral organ abscission, response to farnesol, brassinosteroid mediated signaling pathway, and light-harvesting. The final interactome of A. sisalana contains 2,582 nodes and 15,083 edges. In the reconstructed interactome, we identified submodules related to plant processes to validate the reconstruction. In addition, we identified 6 hub genes that were searched for in the co-expression modules. The intersection of hub genes identified by both the protein-protein interaction networks (PPI networks) and co-expression analyses using gene significance and module membership revealed six potential candidate genes for key genes. In conclusion, we identified six potential key genes for specific studies in Agave transcriptome atlas studies, biological processes related to plant survival in unfavorable environments and provide strategies for breeding programs.
{"title":"Analysis of protein-protein interaction and weighted co-expression networks revealed key modules and genes in multiple organs of Agave sisalana","authors":"L. M. Carvalho, N. V. Silva, L. G. D. de Abreu, Marina Pupke Marone, Alexandra Russolo Cardelli, Fábio Trigo Raya, Guido Araujo, M. Carazzolle, G. G. Guimarães Pereira","doi":"10.3389/fceng.2023.1175235","DOIUrl":"https://doi.org/10.3389/fceng.2023.1175235","url":null,"abstract":"Agave plants are well-known for their drought resilience and commercial applications. Among them, Agave sisalana (sisal) is the species most used to produce hard fibers, and it is of great importance for semiarid regions. Agaves also show potential as bioenergy feedstocks, as they can accumulate large amounts of biomass and fermentable sugar. This study aimed to reconstruct the A. sisalana interactome, and identify key genes and modules involved in multiple plant tissues (root, stem, and leaf) through RNA-Seq analysis. We integrated A. sisalana transcriptome sequences and gene expression generated from stem, leaf, and root tissues to build global and conditional co-expression networks across the entire transcriptome. By combining the co-expression network, module classification, and function enrichment tools, we identified 20 functional modules related to at least one A. sisalana tissue, covering functions such as photosynthesis, leaf formation, auxin-activated signaling pathway, floral organ abscission, response to farnesol, brassinosteroid mediated signaling pathway, and light-harvesting. The final interactome of A. sisalana contains 2,582 nodes and 15,083 edges. In the reconstructed interactome, we identified submodules related to plant processes to validate the reconstruction. In addition, we identified 6 hub genes that were searched for in the co-expression modules. The intersection of hub genes identified by both the protein-protein interaction networks (PPI networks) and co-expression analyses using gene significance and module membership revealed six potential candidate genes for key genes. In conclusion, we identified six potential key genes for specific studies in Agave transcriptome atlas studies, biological processes related to plant survival in unfavorable environments and provide strategies for breeding programs.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43054892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-18DOI: 10.3389/fceng.2023.1193230
A. Beugholt, D. Geier, T. Becker
A variety of yeast applications in the food and beverage industry require individual and reproducible yeast propagation at high yields and consistent quality. One quality-determining parameter for yeast propagation is effective aeration to avoid oxygen depletion. Therefore, this work investigated three important aeration parameters: airflow, pulse time, and oxygen concentration, for their influence on yeast propagation. The aeration of a propagator involves phase transitions which are gradient-driven processes and can be accelerated with higher gradients between the liquid medium and the gas bubbles. In this study, oxygen-enriched air generated with membrane filters was used to aerate the system in an easy and cost-efficient way without the need for expensive technical gas usage. Propagation experiments were carried out in a pilot-scale reactor equipped with a membrane filter system for enhanced oxygen concentrations in ingas and online sensors for representative monitoring of the process. The membrane filter system is based on the separation of nitrogen in compressed air, leading to oxygen enrichment. Using oxygen-enriched air for propagation aeration showed higher oxygen transfer into the medium and the anaerobic process time caused by oxygen depletion due to high cell numbers was reduced by an average of 7.4% for pulsed aeration. Additionally, we conducted experiments with controlled measures of dissolved oxygen using different oxygen concentrations for aeration. The main objective of this study is to present a new and affordable optimization of propagation aeration using membrane filtration to enrich process air. The results showed increased cell counts for higher ingas oxygen concentrations and no negative impact on cell vitality was observed. Hence, our investigations showed that using oxygen-enriched air reduced the frequency of pulsed aeration, thus hindering foam formation, a limiting factor of the yeast propagation process.
{"title":"Improvement of Saccharomyces propagation performance through oxygen-enriched air and aeration parameter variation","authors":"A. Beugholt, D. Geier, T. Becker","doi":"10.3389/fceng.2023.1193230","DOIUrl":"https://doi.org/10.3389/fceng.2023.1193230","url":null,"abstract":"A variety of yeast applications in the food and beverage industry require individual and reproducible yeast propagation at high yields and consistent quality. One quality-determining parameter for yeast propagation is effective aeration to avoid oxygen depletion. Therefore, this work investigated three important aeration parameters: airflow, pulse time, and oxygen concentration, for their influence on yeast propagation. The aeration of a propagator involves phase transitions which are gradient-driven processes and can be accelerated with higher gradients between the liquid medium and the gas bubbles. In this study, oxygen-enriched air generated with membrane filters was used to aerate the system in an easy and cost-efficient way without the need for expensive technical gas usage. Propagation experiments were carried out in a pilot-scale reactor equipped with a membrane filter system for enhanced oxygen concentrations in ingas and online sensors for representative monitoring of the process. The membrane filter system is based on the separation of nitrogen in compressed air, leading to oxygen enrichment. Using oxygen-enriched air for propagation aeration showed higher oxygen transfer into the medium and the anaerobic process time caused by oxygen depletion due to high cell numbers was reduced by an average of 7.4% for pulsed aeration. Additionally, we conducted experiments with controlled measures of dissolved oxygen using different oxygen concentrations for aeration. The main objective of this study is to present a new and affordable optimization of propagation aeration using membrane filtration to enrich process air. The results showed increased cell counts for higher ingas oxygen concentrations and no negative impact on cell vitality was observed. Hence, our investigations showed that using oxygen-enriched air reduced the frequency of pulsed aeration, thus hindering foam formation, a limiting factor of the yeast propagation process.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49274563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-15DOI: 10.3389/fceng.2023.1160254
Liping Zhong, Thi Ha Giang Pham, Youngdon Ko, A. Züttel
Methanation of CO2 is an important reaction for reducing CO2 emissions in a power-to-gas system. Compared to cobalt supported on gamma-Al2O3, cobalt supported on graphene nanoplatelets (GNPs) showed significantly better performance for CO2 methanation. Cobalt supported on GNPs was capable of 15% conversion of CO2 to CH4 at temperatures below 250°C, compared to 5% for cobalt supported on Al2O3. In situ thermogravimetric analysis (TGA) demonstrated that the Co/GNP catalyst was stable to 400°C. The maximum catalyst mass-specific CH4 yield was obtained at a Co loading of 5wt% on GNPs; however, high Co loading on GNPs deactivated the reactivity of the Co/GNP catalyst. Transmission electron microscopy (TEM) demonstrated that 5wt% Co/GNPs had the smallest and most dispersed cobalt nanoparticles. Excessive loading of cobalt tended to form isolated large Co nanoparticles. X-ray photoelectron spectroscopy (XPS) and Raman spectrometry revealed that more CoO phases were maintained on the surface of 5wt% Co/GNPs, indicating that the interaction between the Co and the GNPs had more of an impact on cobalt’s redox capacity than did particle size, which ultimately affected cobalt’s active phase during the CO2 reduction process. Furthermore, Raman spectrometry demonstrated that Co loading led to an increase in graphene defects. Higher Co loading on GNPs resulted in fewer interfaces between Co and GNPs due to the agglomeration of Co nanoparticles.
{"title":"Graphene nanoplatelets promoted CoO-based catalyst for low temperature CO2 methanation reaction","authors":"Liping Zhong, Thi Ha Giang Pham, Youngdon Ko, A. Züttel","doi":"10.3389/fceng.2023.1160254","DOIUrl":"https://doi.org/10.3389/fceng.2023.1160254","url":null,"abstract":"Methanation of CO2 is an important reaction for reducing CO2 emissions in a power-to-gas system. Compared to cobalt supported on gamma-Al2O3, cobalt supported on graphene nanoplatelets (GNPs) showed significantly better performance for CO2 methanation. Cobalt supported on GNPs was capable of 15% conversion of CO2 to CH4 at temperatures below 250°C, compared to 5% for cobalt supported on Al2O3. In situ thermogravimetric analysis (TGA) demonstrated that the Co/GNP catalyst was stable to 400°C. The maximum catalyst mass-specific CH4 yield was obtained at a Co loading of 5wt% on GNPs; however, high Co loading on GNPs deactivated the reactivity of the Co/GNP catalyst. Transmission electron microscopy (TEM) demonstrated that 5wt% Co/GNPs had the smallest and most dispersed cobalt nanoparticles. Excessive loading of cobalt tended to form isolated large Co nanoparticles. X-ray photoelectron spectroscopy (XPS) and Raman spectrometry revealed that more CoO phases were maintained on the surface of 5wt% Co/GNPs, indicating that the interaction between the Co and the GNPs had more of an impact on cobalt’s redox capacity than did particle size, which ultimately affected cobalt’s active phase during the CO2 reduction process. Furthermore, Raman spectrometry demonstrated that Co loading led to an increase in graphene defects. Higher Co loading on GNPs resulted in fewer interfaces between Co and GNPs due to the agglomeration of Co nanoparticles.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49584542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-15DOI: 10.3389/fceng.2023.1157889
H. Narayanan, M. von Stosch, F. Feidl, M. Sokolov, M. Morbidelli, A. Butté
Process models are mathematical formulations (essentially a set of equations) that try to represent the real system/process in a digital or virtual form. These are derived either based on fundamental physical laws often combined with empirical assumptions or learned based on data. The former has been existing for several decades in chemical and process engineering while the latter has recently received a lot of attention with the emergence of several artificial intelligence/machine learning techniques. Hybrid modeling is an emerging modeling paradigm that explores the synergy between existing these two paradigms, taking advantage of the existing process knowledge (or engineering know-how) and information disseminated by the collected data. Such an approach is especially suitable for systems and industries where data generation is significantly resource intensive while at the same time fundamentally not completely deciphered such as the processes involved in the biopharmaceutical pipeline. This technology could, in fact, be the enabler to meeting the demands and goals of several initiatives such as Quality by design, Process Analytical tools, and Pharma 4.0. In addition, it can aid in different process applications throughout process development and Chemistry, Manufacturing, and Control (CMC) to make it more strategic and efficient. This article focuses on providing a step-by-step guide to the different considerations to be made to develop a reliable and applicable hybrid model. In addition, the article aims at highlighting the need for such tools in the biopharmaceutical industry and summarizes the works that advocate its implications. Subsequently, the key qualities of hybrid modeling that make it a key enabler in the biopharmaceutical industry are elaborated with reference to the literature demonstrating such qualities.
{"title":"Hybrid modeling for biopharmaceutical processes: advantages, opportunities, and implementation","authors":"H. Narayanan, M. von Stosch, F. Feidl, M. Sokolov, M. Morbidelli, A. Butté","doi":"10.3389/fceng.2023.1157889","DOIUrl":"https://doi.org/10.3389/fceng.2023.1157889","url":null,"abstract":"Process models are mathematical formulations (essentially a set of equations) that try to represent the real system/process in a digital or virtual form. These are derived either based on fundamental physical laws often combined with empirical assumptions or learned based on data. The former has been existing for several decades in chemical and process engineering while the latter has recently received a lot of attention with the emergence of several artificial intelligence/machine learning techniques. Hybrid modeling is an emerging modeling paradigm that explores the synergy between existing these two paradigms, taking advantage of the existing process knowledge (or engineering know-how) and information disseminated by the collected data. Such an approach is especially suitable for systems and industries where data generation is significantly resource intensive while at the same time fundamentally not completely deciphered such as the processes involved in the biopharmaceutical pipeline. This technology could, in fact, be the enabler to meeting the demands and goals of several initiatives such as Quality by design, Process Analytical tools, and Pharma 4.0. In addition, it can aid in different process applications throughout process development and Chemistry, Manufacturing, and Control (CMC) to make it more strategic and efficient. This article focuses on providing a step-by-step guide to the different considerations to be made to develop a reliable and applicable hybrid model. In addition, the article aims at highlighting the need for such tools in the biopharmaceutical industry and summarizes the works that advocate its implications. Subsequently, the key qualities of hybrid modeling that make it a key enabler in the biopharmaceutical industry are elaborated with reference to the literature demonstrating such qualities.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44213294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.3389/fceng.2023.1150776
Dipen K. Patel, Y. Bhimavarapu, A. Jena, R. Tadmor, Tianxing Cai
Initial methods to detect rust in pipelines have been conducted through invasive probes and sectioning off parts of the facility as the plant is running. These methods greatly increase the costs overall. The need for a feasible solution to this issue lies in the detection of rust formation through a non-invasive method. This study’s objective is to measure rust formation through droplet motion on the outer layer of pipelines. Multiple experiments are conducted using carbon steel sheets whose bottom layer has been exposed to acid for different durations of time. As rust formation in the metal is a voltaic phenomenon, it would mean that the acid corrosion of the bottom layer would adversely affect the top layer of the substrate. Consequentially, droplet motion and the droplet’s contour would change in different corrosive scenarios which we could then detect with novel parameters in our lab. One such parameter is the Interfacial Modulus (GS), which describes the initial resistance of the solid’s outer layer towards the liquid. We can understand this parameter with the aid of the novel device, known as the Centrifugal Adhesion Balance (CAB). As we cause the drop to slide across the substrate at constant normal force condition, we observe the difference in the contour of the drop as it slides across the substrate. The real-time change in contact angles at each edge of the drop, along with its change in external lateral force, causes a change in the GS values, which varies in different corrosive scenarios.
{"title":"Non-invasive rust detection of steel plates determined through interfacial modulus","authors":"Dipen K. Patel, Y. Bhimavarapu, A. Jena, R. Tadmor, Tianxing Cai","doi":"10.3389/fceng.2023.1150776","DOIUrl":"https://doi.org/10.3389/fceng.2023.1150776","url":null,"abstract":"Initial methods to detect rust in pipelines have been conducted through invasive probes and sectioning off parts of the facility as the plant is running. These methods greatly increase the costs overall. The need for a feasible solution to this issue lies in the detection of rust formation through a non-invasive method. This study’s objective is to measure rust formation through droplet motion on the outer layer of pipelines. Multiple experiments are conducted using carbon steel sheets whose bottom layer has been exposed to acid for different durations of time. As rust formation in the metal is a voltaic phenomenon, it would mean that the acid corrosion of the bottom layer would adversely affect the top layer of the substrate. Consequentially, droplet motion and the droplet’s contour would change in different corrosive scenarios which we could then detect with novel parameters in our lab. One such parameter is the Interfacial Modulus (GS), which describes the initial resistance of the solid’s outer layer towards the liquid. We can understand this parameter with the aid of the novel device, known as the Centrifugal Adhesion Balance (CAB). As we cause the drop to slide across the substrate at constant normal force condition, we observe the difference in the contour of the drop as it slides across the substrate. The real-time change in contact angles at each edge of the drop, along with its change in external lateral force, causes a change in the GS values, which varies in different corrosive scenarios.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42840457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.3389/fceng.2023.1186878
Avinashkumar V. Karre, Tianxing Cai
Biochar has been found to be an effective material for the removal of nitrobenzene from both aqueous and soil phases. Some innovative uses of biochar in environmental applications for nitrobenzene removal include: 1) Biochar amendments for soil remediation. 2) Biochar for water treatment. 3) Biochar-based adsorbents. 4) Biochar-based membranes. Therefore, biochar is a promising material for the removal of nitrobenzene from both aqueous and soil phases, and its innovative uses in environmental applications continue to be explored. This paper presents the toxicity of nitrobenzene and potential hazards, with a discussion on the motivation and recent resolutions for nitrobenzene removal in aqueous and soil phases. Methodological cornerstones of innovative uses of biochar in environmental applications for nitrobenzene removal in aqueous and soil phases are introduced and reviewed. Overview and perspectives for the corresponding application are also provided. The innovative uses of biochar in environmental applications for nitrobenzene removal in aqueous and soil phases can bring new insights and add tremendous value to environmental chemical engineering.
{"title":"Review of innovative uses of biochar in environmental applications for nitrobenzene removal in aqueous and soil phases","authors":"Avinashkumar V. Karre, Tianxing Cai","doi":"10.3389/fceng.2023.1186878","DOIUrl":"https://doi.org/10.3389/fceng.2023.1186878","url":null,"abstract":"Biochar has been found to be an effective material for the removal of nitrobenzene from both aqueous and soil phases. Some innovative uses of biochar in environmental applications for nitrobenzene removal include: 1) Biochar amendments for soil remediation. 2) Biochar for water treatment. 3) Biochar-based adsorbents. 4) Biochar-based membranes. Therefore, biochar is a promising material for the removal of nitrobenzene from both aqueous and soil phases, and its innovative uses in environmental applications continue to be explored. This paper presents the toxicity of nitrobenzene and potential hazards, with a discussion on the motivation and recent resolutions for nitrobenzene removal in aqueous and soil phases. Methodological cornerstones of innovative uses of biochar in environmental applications for nitrobenzene removal in aqueous and soil phases are introduced and reviewed. Overview and perspectives for the corresponding application are also provided. The innovative uses of biochar in environmental applications for nitrobenzene removal in aqueous and soil phases can bring new insights and add tremendous value to environmental chemical engineering.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44671690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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