Nerve guidance conduits (NGCs) have been shown to be effective in promoting nerve regeneration in a variety of clinical applications, including nerve defects resulting from a trauma or surgery. By providing a conducive environment for nerve growth, NGCs can help to restore function in nerve‐damaged patients. Challenges include limited repair length, difficulty replicating natural nerve, and rapid substance degradation affecting neurotrophic factor delivery. Considering these issues with mass transfer and fluid structure interaction (FSI) emphasizes the need for enhancing nerve regeneration efficiency. To facilitate nerve growth and deliver appropriate amount of growth factors, these conduits need to be designed with specific topological, mechanical, and biological properties. Additionally, considerations must be given to functional mass transfer FSI design. An intelligent NGC design is proposed as an evaluation‐optimization and AI‐based method. It is found that design parameters significantly impact the physical properties being optimized, including hydraulic pressure, porosity, diffusivity, water absorption, and maximum stress. The mathematical surrogate model obtained from data‐based modelling is used for artificial intelligence (AI) optimization algorithms, differential evolution (DE), and non‐dominated sorting genetic algorithm II (NSGA‐II). It is revealed that both DE and NSGA algorithms generate nearly identical solutions, ensuring the robustness of ML optimization. Our results show that NGC with the thickness of 750 μm results in more than 170% augmentation of porosity. Moreover, at a constant ovality, increasing the channel thickness results in more than 39.2% augmentation of the maximum stress. The accurate forecasting of physical characteristics on NGC regarding nerve growth factors enables a hopeful outlook for the future clinical treatment of nerve injuries and advanced tissue engineering.
神经引导导管（NGCs）已被证明能有效促进多种临床应用中的神经再生，包括创伤或手术导致的神经缺损。通过为神经生长提供有利环境，NGCs 可以帮助神经受损患者恢复功能。所面临的挑战包括修复长度有限、难以复制天然神经以及物质降解过快影响神经营养因子的输送。考虑到这些问题与传质和流体结构相互作用（FSI）的关系，提高神经再生效率的必要性就显得尤为重要。为了促进神经生长并输送适量的生长因子，这些导管的设计需要具备特定的拓扑、机械和生物特性。此外，还必须考虑功能性传质 FSI 设计。本文提出了一种基于评估优化和人工智能的智能 NGC 设计方法。研究发现，设计参数对优化的物理特性有重大影响，包括水压、孔隙率、扩散率、吸水性和最大应力。基于数据建模获得的数学代用模型被用于人工智能（AI）优化算法、微分进化算法（DE）和非支配排序遗传算法 II（NSGA-II）。结果表明，微分进化算法和非支配排序遗传算法 II（NSGA-II）产生的解决方案几乎完全相同，从而确保了 ML 优化的稳健性。结果表明，厚度为 750 μm 的 NGC 可使孔隙率增加 170% 以上。此外，在椭圆度不变的情况下，增加通道厚度可使最大应力增加 39.2% 以上。准确预测神经生长因子在 NGC 上的物理特性，为未来临床治疗神经损伤和先进的组织工程学带来了希望。
{"title":"Intelligent design of nerve guidance conduits: An artificial intelligence‐driven fluid structure interaction study on modelling and optimization of nerve growth","authors":"Faridoddin Hassani, Ali Golshani, Raman Mehrabi, Afshin Kouhkord, Mojtaba Guilani, Mahkame Sharbatdar","doi":"10.1002/cjce.25490","DOIUrl":"https://doi.org/10.1002/cjce.25490","url":null,"abstract":"Nerve guidance conduits (NGCs) have been shown to be effective in promoting nerve regeneration in a variety of clinical applications, including nerve defects resulting from a trauma or surgery. By providing a conducive environment for nerve growth, NGCs can help to restore function in nerve‐damaged patients. Challenges include limited repair length, difficulty replicating natural nerve, and rapid substance degradation affecting neurotrophic factor delivery. Considering these issues with mass transfer and fluid structure interaction (FSI) emphasizes the need for enhancing nerve regeneration efficiency. To facilitate nerve growth and deliver appropriate amount of growth factors, these conduits need to be designed with specific topological, mechanical, and biological properties. Additionally, considerations must be given to functional mass transfer FSI design. An intelligent NGC design is proposed as an evaluation‐optimization and AI‐based method. It is found that design parameters significantly impact the physical properties being optimized, including hydraulic pressure, porosity, diffusivity, water absorption, and maximum stress. The mathematical surrogate model obtained from data‐based modelling is used for artificial intelligence (AI) optimization algorithms, differential evolution (DE), and non‐dominated sorting genetic algorithm II (NSGA‐II). It is revealed that both DE and NSGA algorithms generate nearly identical solutions, ensuring the robustness of ML optimization. Our results show that NGC with the thickness of 750 μm results in more than 170% augmentation of porosity. Moreover, at a constant ovality, increasing the channel thickness results in more than 39.2% augmentation of the maximum stress. The accurate forecasting of physical characteristics on NGC regarding nerve growth factors enables a hopeful outlook for the future clinical treatment of nerve injuries and advanced tissue engineering.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249730","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}
Coal water slurry is an advanced and efficient clean coal technology; using gasification wastewater to prepare coal water slurry can recycle wastewater and improve energy utilization efficiency. As the complex substances in wastewater have a great influence on the slurry properties, the effects of organic matter, metal ions, and ammonia nitrogen in gasification wastewater on the surface properties of coal water slurry are studied in this paper in order to provide new ideas for slurry mechanism of coal water slurry prepared from wastewater. Results show the following: (a) Compared with ordinary coal water slurry, the concentration of coal water slurry prepared from wastewater with high organic content increased by 2.9%, while the concentration of coal water slurry prepared from wastewater with high ammonia nitrogen content decreased by 2.1%. (b) The contact angles of coal water slurry prepared with phenols, alcohols, and urethane are reduced by 2.8°, 6.3°, and 1.5°, respectively, so organic matter can change the hydrophilicity of coal particles and affect slurryability. (c) Mg2+ and Ca2+ have basically no effect on slurry. Fe3+ reduces the absolute value of Zeta potential by 33.1, and Cu3+ increases that by 22.8, as they affect the slurryability by changing the surface potential of coal particles and the absorption of additives. (d) Ammonia nitrogen influences the slurryability by changing the pH value of the slurry. The conclusion of the influence mechanism of organic matter, metal ions, and ammonia nitrogen in wastewater on slurryability can provide a technical reference for the selection of suitable wastewater to prepare coal water slurry.
{"title":"Effect of the main components in gasification wastewater on the surface properties of coal water slurry","authors":"Dedi Li, Biao Feng, Yuanlin Luo, Jianbin Wang, Xinjie Lai, Jun Zhao","doi":"10.1002/cjce.25494","DOIUrl":"https://doi.org/10.1002/cjce.25494","url":null,"abstract":"Coal water slurry is an advanced and efficient clean coal technology; using gasification wastewater to prepare coal water slurry can recycle wastewater and improve energy utilization efficiency. As the complex substances in wastewater have a great influence on the slurry properties, the effects of organic matter, metal ions, and ammonia nitrogen in gasification wastewater on the surface properties of coal water slurry are studied in this paper in order to provide new ideas for slurry mechanism of coal water slurry prepared from wastewater. Results show the following: (a) Compared with ordinary coal water slurry, the concentration of coal water slurry prepared from wastewater with high organic content increased by 2.9%, while the concentration of coal water slurry prepared from wastewater with high ammonia nitrogen content decreased by 2.1%. (b) The contact angles of coal water slurry prepared with phenols, alcohols, and urethane are reduced by 2.8°, 6.3°, and 1.5°, respectively, so organic matter can change the hydrophilicity of coal particles and affect slurryability. (c) Mg<jats:sup>2+</jats:sup> and Ca<jats:sup>2+</jats:sup> have basically no effect on slurry. Fe<jats:sup>3+</jats:sup> reduces the absolute value of Zeta potential by 33.1, and Cu<jats:sup>3+</jats:sup> increases that by 22.8, as they affect the slurryability by changing the surface potential of coal particles and the absorption of additives. (d) Ammonia nitrogen influences the slurryability by changing the pH value of the slurry. The conclusion of the influence mechanism of organic matter, metal ions, and ammonia nitrogen in wastewater on slurryability can provide a technical reference for the selection of suitable wastewater to prepare coal water slurry.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249449","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}
Natural gas is the cleanest fossil energy source and its consumption is increasing rapidly, so an efficient natural gas way of storing and transporting is urgently needed. Solidified natural gas (SNG) technology is gaining traction because of its higher safety, lower cost, and flexible storage and transportation modes. To improve the methane uptake rate in SNG technology, this work investigated the growth of methane hydrate in fatty alcohol polyoxyethylene ether sodium sulphate (AES) solution with the addition of three different pores per inch (PPI) of copper foam (CF). The results showed that the addition of AES caused the hydrate to grow upwards along the wall, and the methane uptake in the 300 ppm AES solution was increased by 623% compared to pure water. CF not only provided more nucleation sites for hydrate but also transferred the heat generated during hydration. Moreover, there was a synergistic effect between AES and CF and the solution could continuously transport upward along the continuous metal skeleton to increase the gas–liquid contact area. Thus, the formation rate and induction time of methane hydrate improve. Hydrate had the highest methane uptake in the 20 PPI CF system and the lower the pressure, the greater the ability of CF to promote hydrate formation. The methane uptake improved by 27.6% and the induction time was reduced by 59.7% compared to the pure AES system at 6 MPa. This work is aimed at advancing SNG technology (especially at low pressure) and informs the theoretical foundation.
{"title":"Synergistic effect of alcohol polyoxyethylene ether sodium sulphate and copper foam on methane hydrate formation","authors":"Hao Wang, Guiyang Ma, Zhongsheng Wang, Jinping Yu, Xiangchun Jiang","doi":"10.1002/cjce.25500","DOIUrl":"https://doi.org/10.1002/cjce.25500","url":null,"abstract":"Natural gas is the cleanest fossil energy source and its consumption is increasing rapidly, so an efficient natural gas way of storing and transporting is urgently needed. Solidified natural gas (SNG) technology is gaining traction because of its higher safety, lower cost, and flexible storage and transportation modes. To improve the methane uptake rate in SNG technology, this work investigated the growth of methane hydrate in fatty alcohol polyoxyethylene ether sodium sulphate (AES) solution with the addition of three different pores per inch (PPI) of copper foam (CF). The results showed that the addition of AES caused the hydrate to grow upwards along the wall, and the methane uptake in the 300 ppm AES solution was increased by 623% compared to pure water. CF not only provided more nucleation sites for hydrate but also transferred the heat generated during hydration. Moreover, there was a synergistic effect between AES and CF and the solution could continuously transport upward along the continuous metal skeleton to increase the gas–liquid contact area. Thus, the formation rate and induction time of methane hydrate improve. Hydrate had the highest methane uptake in the 20 PPI CF system and the lower the pressure, the greater the ability of CF to promote hydrate formation. The methane uptake improved by 27.6% and the induction time was reduced by 59.7% compared to the pure AES system at 6 MPa. This work is aimed at advancing SNG technology (especially at low pressure) and informs the theoretical foundation.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249448","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}
Abdul Zahir, Perumal Kumar, Agus Saptoro, Milinkumar Shah, Angnes Ngieng Tze Tiong, Jundika Candra Kurnia, Samreen Hameed
The current study developed a novel computational fluid dynamics (CFD) model that accounted for both physical and chemical absorption in the multiphase flow and captured the relative dominance of chemical absorption over physical by employing a tunable model parameter ‘enhancement factor’. The CFD model was validated against experimental data in a rotating packed bed, and then the validated model was used to investigate the effect of operational parameters such as rotational speed, monoethanolamine (MEA) concentration, inlet velocity, and MEA‐packing contact angle on the physiochemical absorption. The significance of each operational parameter was then evaluated by the ANOVA analysis, which inferred that the enhancement factor is sensitive to rotational speed, MEA concentration, inlet velocity, and contact angle. The p‐value of MEA concentration and inlet velocity was less than 0.05, which implies that these two variables are the most significant variables for the chemical absorption of CO2. The response surface methodology (RSM) and the artificial neural network (ANN) were also employed to develop the predictive model for the enhancement factor. Among the employed techniques, ANN resulted in R2 closer to 0.99 and could better predict the enhancement factor. The modelling approach and findings of the current study are useful in optimizing the operation of rotating packed‐bed reactor (RPB) for CO2 absorption on the industrial scale.
{"title":"Computational modelling and optimization of physicochemical absorption of CO2 in rotating packed bed","authors":"Abdul Zahir, Perumal Kumar, Agus Saptoro, Milinkumar Shah, Angnes Ngieng Tze Tiong, Jundika Candra Kurnia, Samreen Hameed","doi":"10.1002/cjce.25495","DOIUrl":"https://doi.org/10.1002/cjce.25495","url":null,"abstract":"The current study developed a novel computational fluid dynamics (CFD) model that accounted for both physical and chemical absorption in the multiphase flow and captured the relative dominance of chemical absorption over physical by employing a tunable model parameter ‘enhancement factor’. The CFD model was validated against experimental data in a rotating packed bed, and then the validated model was used to investigate the effect of operational parameters such as rotational speed, monoethanolamine (MEA) concentration, inlet velocity, and MEA‐packing contact angle on the physiochemical absorption. The significance of each operational parameter was then evaluated by the ANOVA analysis, which inferred that the enhancement factor is sensitive to rotational speed, MEA concentration, inlet velocity, and contact angle. The <jats:italic>p</jats:italic>‐value of MEA concentration and inlet velocity was less than 0.05, which implies that these two variables are the most significant variables for the chemical absorption of CO<jats:sub>2</jats:sub>. The response surface methodology (RSM) and the artificial neural network (ANN) were also employed to develop the predictive model for the enhancement factor. Among the employed techniques, ANN resulted in <jats:italic>R</jats:italic><jats:sup>2</jats:sup> closer to 0.99 and could better predict the enhancement factor. The modelling approach and findings of the current study are useful in optimizing the operation of rotating packed‐bed reactor (RPB) for CO<jats:sub>2</jats:sub> absorption on the industrial scale.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268389","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}
There is a lack of consideration of temporal and spatial correlation in the process variables and adjacent hidden layers correlation in the soft sensor model of stacked autoencoders. To address the issue, a novel global dynamic adjacent layer information enhancement auto encoder (GD‐ALIEAE) method is proposed to improve the poor prediction performance. The gated recurrent unit (GRU) and uniform manifold approximation and projection (UMAP) are applied to the GD‐ALIEAE model for obtaining global dynamic features of the temporal and spatial information of process variables by parallel computation. An adjacent layer information correlation algorithm is proposed to avoid the loss of hidden layers information during the stacking process. The algorithm enhances the features of the low layer through nonlinear mapping, combining the low layer and its adjacent layer as input. The input then is fed to the multi‐head attention mechanism to obtain features that contain adjacent layer correlation. Finally, a prediction model is established through a fully connected layer. Through simulation experiments on two industrial cases of sulphur recovery unit and thermal power plant, and compared with models of stacked autoencoder (SAE), stacked isomorphic autoencoder (SIAE), and target‐related stacked autoencoder (TSAE), the effectiveness of the proposed method was verified.
{"title":"Global dynamic features and information of adjacent hidden layer enhancement based on autoencoder for industrial process soft sensor application","authors":"Xiaoping Guo, Sulei Pan, Yuan Li","doi":"10.1002/cjce.25483","DOIUrl":"https://doi.org/10.1002/cjce.25483","url":null,"abstract":"There is a lack of consideration of temporal and spatial correlation in the process variables and adjacent hidden layers correlation in the soft sensor model of stacked autoencoders. To address the issue, a novel global dynamic adjacent layer information enhancement auto encoder (GD‐ALIEAE) method is proposed to improve the poor prediction performance. The gated recurrent unit (GRU) and uniform manifold approximation and projection (UMAP) are applied to the GD‐ALIEAE model for obtaining global dynamic features of the temporal and spatial information of process variables by parallel computation. An adjacent layer information correlation algorithm is proposed to avoid the loss of hidden layers information during the stacking process. The algorithm enhances the features of the low layer through nonlinear mapping, combining the low layer and its adjacent layer as input. The input then is fed to the multi‐head attention mechanism to obtain features that contain adjacent layer correlation. Finally, a prediction model is established through a fully connected layer. Through simulation experiments on two industrial cases of sulphur recovery unit and thermal power plant, and compared with models of stacked autoencoder (SAE), stacked isomorphic autoencoder (SIAE), and target‐related stacked autoencoder (TSAE), the effectiveness of the proposed method was verified.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249450","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}
Sudhir S. Gandhi, Parag R. Gogate, Abhijeet D. Patil
The current work illustrates a novel method of pre‐treating rice straw using ultrasound (US) as well as using ultrasound coupled with anaerobic digestion (AD) to intensify biogas production. The primary objectives were to evaluate the effectiveness of ultrasound in increasing the utilization of rice straw and to optimize the conditions for maximum biogas yield. Important parameters such as ultrasonic power (0.2–1 W/mL), duty cycle (20%–80%), and substrate loading (2%–10% w/v) were varied to understand their effects during pre‐treatment. The results showed that the maximum increase in soluble chemical oxygen demand (sCOD), with a final value of 13,500 mg/L (an increase of 64.63%), was achieved under optimum conditions of ultrasonic power of 0.4 W/mL, a duty cycle of 50%, and a substrate loading of 6% w/v. Additionally, the study evaluated the effect of low‐intensity US exposure during AD with pre‐treated rice straw at varying irradiation times (10–30 min) and duty cycles (20%–60%). The optimal conditions of ultrasonic time of 20 min and a duty cycle of 50% resulted in nearly four times higher biogas generation compared to untreated samples. The current research successfully demonstrates the efficient use of US in the feedstock pre‐treatment and also in AD process, leading to significant intensification in biogas production within a shorter time frame.
{"title":"Intensification of biogas production from rice straw using anaerobic digestion based on pre‐treatment with ultrasound","authors":"Sudhir S. Gandhi, Parag R. Gogate, Abhijeet D. Patil","doi":"10.1002/cjce.25489","DOIUrl":"https://doi.org/10.1002/cjce.25489","url":null,"abstract":"The current work illustrates a novel method of pre‐treating rice straw using ultrasound (US) as well as using ultrasound coupled with anaerobic digestion (AD) to intensify biogas production. The primary objectives were to evaluate the effectiveness of ultrasound in increasing the utilization of rice straw and to optimize the conditions for maximum biogas yield. Important parameters such as ultrasonic power (0.2–1 W/mL), duty cycle (20%–80%), and substrate loading (2%–10% w/v) were varied to understand their effects during pre‐treatment. The results showed that the maximum increase in soluble chemical oxygen demand (sCOD), with a final value of 13,500 mg/L (an increase of 64.63%), was achieved under optimum conditions of ultrasonic power of 0.4 W/mL, a duty cycle of 50%, and a substrate loading of 6% w/v. Additionally, the study evaluated the effect of low‐intensity US exposure during AD with pre‐treated rice straw at varying irradiation times (10–30 min) and duty cycles (20%–60%). The optimal conditions of ultrasonic time of 20 min and a duty cycle of 50% resulted in nearly four times higher biogas generation compared to untreated samples. The current research successfully demonstrates the efficient use of US in the feedstock pre‐treatment and also in AD process, leading to significant intensification in biogas production within a shorter time frame.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195432","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}
Azizul Buang, Muhammad Ameer Zaaba, Muhammad Izham Mohd Yusof, Daneskumar Manogaran, Hani Tiara Faihana Hifni, Muhammad Roil Bilad
Boilover can occur several hours after the fuel in a storage tank caught fire. The delayed occurrence is an unknown strong parameter when managing the emergency response operations. Those managing response operations must be aware of the boilover potential and take the precautions to ensure safety. Modelling the phenomenon enables predicting crucial event features and assists in highlighting safety measures, with a key focus on the ignition‐to‐boilover time interval. This study focused on the predictive empirical tool development aimed at estimating the boilover onset time and consequences. This was achieved through series of small‐scale boilover experiments, followed by validation using cases of boilover incidents. The results revealed a linear relationship between the boilover onset time and the initial depth of fuel. Consequently, an empirical correlation was derived to predict the time to boilover. The developed correlation has demonstrated its ability to offer conservative predictions while also exhibiting agreement with both the observed onset time and consequences of boilover events. The reported time to boilover for the Czechowice‐Dziedzice incident is 1050 min, while the predicted time is 1413.2 min. The model showed reasonable agreement with the Amoco Refinery incident. The predicted boilover time of 811.3 min aligns with the boilover incident, reported as 790 and 925 min, respectively. It is evident that the empirical model can predict the time to boilover to a similar order of magnitude. Certain considerations in the development of effective strategies in handling fire scenario with boilover potentials can be assessed using the predictive tool developed.
{"title":"Empirical prediction on boilover onset and impact for liquid hydrocarbon fire in atmospheric storage tank","authors":"Azizul Buang, Muhammad Ameer Zaaba, Muhammad Izham Mohd Yusof, Daneskumar Manogaran, Hani Tiara Faihana Hifni, Muhammad Roil Bilad","doi":"10.1002/cjce.25485","DOIUrl":"https://doi.org/10.1002/cjce.25485","url":null,"abstract":"Boilover can occur several hours after the fuel in a storage tank caught fire. The delayed occurrence is an unknown strong parameter when managing the emergency response operations. Those managing response operations must be aware of the boilover potential and take the precautions to ensure safety. Modelling the phenomenon enables predicting crucial event features and assists in highlighting safety measures, with a key focus on the ignition‐to‐boilover time interval. This study focused on the predictive empirical tool development aimed at estimating the boilover onset time and consequences. This was achieved through series of small‐scale boilover experiments, followed by validation using cases of boilover incidents. The results revealed a linear relationship between the boilover onset time and the initial depth of fuel. Consequently, an empirical correlation was derived to predict the time to boilover. The developed correlation has demonstrated its ability to offer conservative predictions while also exhibiting agreement with both the observed onset time and consequences of boilover events. The reported time to boilover for the Czechowice‐Dziedzice incident is 1050 min, while the predicted time is 1413.2 min. The model showed reasonable agreement with the Amoco Refinery incident. The predicted boilover time of 811.3 min aligns with the boilover incident, reported as 790 and 925 min, respectively. It is evident that the empirical model can predict the time to boilover to a similar order of magnitude. Certain considerations in the development of effective strategies in handling fire scenario with boilover potentials can be assessed using the predictive tool developed.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195238","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}
Measuring polyethylene properties in the laboratory is time‐consuming and usually unavailable in real‐time, posing significant challenges for controlling product quality in polymerization plants. This research focuses on developing multivariate data‐driven soft sensors for online monitoring and prediction of key characteristics. The targeted properties for prediction include the melt flow index (MFI), density, and average particle diameter in the gas‐phase fluidized bed reactor, as well as the MFI and flow rate ratio (FRR) in the slurry‐phase process. We conducted an exhaustive examination using an ensemble learning approach to quantify the impact of process variables on the model's responses. Various machine learning (ML) algorithms were trained and validated using datasets from industrial ethylene polymerization plants. The precision of the ML models was improved by splitting the datasets into categories comprising high and low MFI and FRR, as well as linear low‐density and high‐density clusters. Then, segmented ML models were developed for each cluster. The results demonstrated that the segmented ML models utilizing optimized Gaussian process regression models with suitable kernel functions and ensemble bagged tree models offered the highest accuracy in predicting the MFI, FRR, and density. Additionally, the comprehensive ML model without clustering, utilizing Gaussian process regression with an isotropic exponential kernel function, proved to be the most effective at predicting the average particle diameter.
在实验室测量聚乙烯特性非常耗时，而且通常无法实时测量，这给聚合工厂的产品质量控制带来了巨大挑战。这项研究的重点是开发多元数据驱动的软传感器，用于在线监测和预测关键特性。预测的目标特性包括气相流化床反应器中的熔体流动指数（MFI）、密度和平均颗粒直径，以及浆相工艺中的熔体流动指数和流速比（FRR）。我们使用集合学习方法进行了详尽的检查，以量化工艺变量对模型响应的影响。我们使用来自工业乙烯聚合工厂的数据集对各种机器学习（ML）算法进行了训练和验证。通过将数据集划分为高和低 MFI 和 FRR 类别，以及线性低密度和高密度聚类，提高了 ML 模型的精度。然后，为每个聚类开发了分段 ML 模型。结果表明，利用具有适当核函数的优化高斯过程回归模型和集合袋装树模型的分段 ML 模型在预测 MFI、FRR 和密度方面具有最高的准确性。此外，利用具有各向同性指数核函数的高斯过程回归的无聚类综合 ML 模型在预测颗粒平均直径方面被证明是最有效的。
{"title":"Enhancing quality control of polyethylene in industrial polymerization plants through predictive multivariate data‐driven soft sensors","authors":"Farzad Jani, Shahin Hosseini, Abdolhannan Sepahi, Seyyed Kamal Afzali, Farzad Torabi, Rooholla Ghorbani, Saeed Houshmandmoayed","doi":"10.1002/cjce.25479","DOIUrl":"https://doi.org/10.1002/cjce.25479","url":null,"abstract":"Measuring polyethylene properties in the laboratory is time‐consuming and usually unavailable in real‐time, posing significant challenges for controlling product quality in polymerization plants. This research focuses on developing multivariate data‐driven soft sensors for online monitoring and prediction of key characteristics. The targeted properties for prediction include the melt flow index (MFI), density, and average particle diameter in the gas‐phase fluidized bed reactor, as well as the MFI and flow rate ratio (FRR) in the slurry‐phase process. We conducted an exhaustive examination using an ensemble learning approach to quantify the impact of process variables on the model's responses. Various machine learning (ML) algorithms were trained and validated using datasets from industrial ethylene polymerization plants. The precision of the ML models was improved by splitting the datasets into categories comprising high and low MFI and FRR, as well as linear low‐density and high‐density clusters. Then, segmented ML models were developed for each cluster. The results demonstrated that the segmented ML models utilizing optimized Gaussian process regression models with suitable kernel functions and ensemble bagged tree models offered the highest accuracy in predicting the MFI, FRR, and density. Additionally, the comprehensive ML model without clustering, utilizing Gaussian process regression with an isotropic exponential kernel function, proved to be the most effective at predicting the average particle diameter.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195235","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}
Ting Jiang, Jin Wang, Yuhan Qin, Chao Hu, Yue Ma, Lin Yang, Xingjian Kong, Linsen Wei
This study introduces a novel technology for continuous vanadium precipitation, aiming to resolve issues such as poor stack density, small particle size, and irregular morphology of ammonium polyvanadate in traditional intermittent processes. In this research, we optimized the process parameters for continuous vanadium precipitation and investigated the mechanism of continuous ammonium polyvanadate crystallization using the focused beam reflectometer measurement. Results showed that small, flaky ammonium polyvanadate particles initially formed between 0 and 12 min. These particles subsequently interlayered and aggregated, resulting in larger particles from 13 to 23 min. By 24 to 60 min, a dynamic equilibrium was reached in crystal growth, aggregation, de‐embedding, and fragmentation. Kinetic analyses demonstrated that increasing the reaction temperature shifted crystal growth from surface reaction control to diffusion control. At higher temperatures, explosive nucleation of ammonium polyvanadate, crystal fragmentation, and dissolution occurred. By integrating the crystallization mechanism, we produced dense ellipsoidal ammonium polyvanadate particles with a stacking density of 0.772 g/cm3 and an average size of 107.04 μm under optimal conditions, achieving a vanadium precipitation rate exceeding 99.0%. Simulation results confirmed that the deflector tube baffle crystallizer enabled continuous crystallization of ammonium polyvanadate, ensuring an average residence time of over 10 min for particles of 50 and 100 μm, facilitating their growth to at least 100 μm. This research provides data and theoretical support for the industrial application of continuous vanadium precipitation.
{"title":"A technique for continuous crystallization of high‐quality ammonium polyvanadate: Crystallization mechanism and simulation of deflector tube baffle crystallizer","authors":"Ting Jiang, Jin Wang, Yuhan Qin, Chao Hu, Yue Ma, Lin Yang, Xingjian Kong, Linsen Wei","doi":"10.1002/cjce.25488","DOIUrl":"https://doi.org/10.1002/cjce.25488","url":null,"abstract":"This study introduces a novel technology for continuous vanadium precipitation, aiming to resolve issues such as poor stack density, small particle size, and irregular morphology of ammonium polyvanadate in traditional intermittent processes. In this research, we optimized the process parameters for continuous vanadium precipitation and investigated the mechanism of continuous ammonium polyvanadate crystallization using the focused beam reflectometer measurement. Results showed that small, flaky ammonium polyvanadate particles initially formed between 0 and 12 min. These particles subsequently interlayered and aggregated, resulting in larger particles from 13 to 23 min. By 24 to 60 min, a dynamic equilibrium was reached in crystal growth, aggregation, de‐embedding, and fragmentation. Kinetic analyses demonstrated that increasing the reaction temperature shifted crystal growth from surface reaction control to diffusion control. At higher temperatures, explosive nucleation of ammonium polyvanadate, crystal fragmentation, and dissolution occurred. By integrating the crystallization mechanism, we produced dense ellipsoidal ammonium polyvanadate particles with a stacking density of 0.772 g/cm<jats:sup>3</jats:sup> and an average size of 107.04 μm under optimal conditions, achieving a vanadium precipitation rate exceeding 99.0%. Simulation results confirmed that the deflector tube baffle crystallizer enabled continuous crystallization of ammonium polyvanadate, ensuring an average residence time of over 10 min for particles of 50 and 100 μm, facilitating their growth to at least 100 μm. This research provides data and theoretical support for the industrial application of continuous vanadium precipitation.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195258","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}
Maria Lima, Jorge Otávio Trierweiler, Marcelo Farenzena
This paper introduces an approach for determining a minimum variance control (MVC) benchmark for nonminimum phase (NMP) multi‐input multi‐output (MIMO) systems using closed‐loop operational data. The MVC benchmark is derived from the MVC law of DBFact factorization introduced by Lima, Trierweiler, and Farenzena. Unlike other factorization methods, DBFact offers advantages such as non‐iterative computation and ensuring internal stability of the MVC law. This approach considers the inherent directionality of NMP MIMO systems, enhancing the reliability of the control performance index. However, the original method relies on prior knowledge of the process model. To overcome this limitation, this paper proposes a method for calculating the MVC benchmark when prior knowledge is absent. It introduces a MIMO system identification strategy employing minimally invasive signal tests. The methodology is evaluated across various control conditions using a quadruple‐tank plant with additional time delays. The study emphasizes the importance of directionality in assessing MIMO system performance, particularly in evaluating individual loop performances. Results demonstrate the identification procedure's effectiveness in accurately calculating the proposed MVC benchmark, even with a mere 1% increase in output variance considered.
本文介绍了一种利用闭环运行数据确定非最小相位（NMP）多输入多输出（MIMO）系统最小方差控制（MVC）基准的方法。MVC 基准源自 Lima、Trierweiler 和 Farenzena 提出的 DBFact 因式分解 MVC 法。与其他因式分解方法不同，DBFact 具有非迭代计算和确保 MVC 法则内部稳定性等优势。这种方法考虑了 NMP MIMO 系统固有的方向性，提高了控制性能指标的可靠性。然而，原始方法依赖于过程模型的先验知识。为了克服这一局限性，本文提出了一种在缺乏先验知识的情况下计算 MVC 基准的方法。它介绍了一种采用微创信号测试的 MIMO 系统识别策略。该方法通过一个具有额外时间延迟的四重罐工厂，在各种控制条件下进行了评估。研究强调了方向性在评估 MIMO 系统性能中的重要性，尤其是在评估单个环路性能时。结果表明，即使考虑的输出方差仅增加 1%，识别程序也能有效准确地计算所提出的 MVC 基准。
{"title":"DBFact applied to minimum variance performance assessment for nonminimum phase multivariate systems from closed‐loop data","authors":"Maria Lima, Jorge Otávio Trierweiler, Marcelo Farenzena","doi":"10.1002/cjce.25492","DOIUrl":"https://doi.org/10.1002/cjce.25492","url":null,"abstract":"This paper introduces an approach for determining a minimum variance control (MVC) benchmark for nonminimum phase (NMP) multi‐input multi‐output (MIMO) systems using closed‐loop operational data. The MVC benchmark is derived from the MVC law of DBFact factorization introduced by Lima, Trierweiler, and Farenzena. Unlike other factorization methods, DBFact offers advantages such as non‐iterative computation and ensuring internal stability of the MVC law. This approach considers the inherent directionality of NMP MIMO systems, enhancing the reliability of the control performance index. However, the original method relies on prior knowledge of the process model. To overcome this limitation, this paper proposes a method for calculating the MVC benchmark when prior knowledge is absent. It introduces a MIMO system identification strategy employing minimally invasive signal tests. The methodology is evaluated across various control conditions using a quadruple‐tank plant with additional time delays. The study emphasizes the importance of directionality in assessing MIMO system performance, particularly in evaluating individual loop performances. Results demonstrate the identification procedure's effectiveness in accurately calculating the proposed MVC benchmark, even with a mere 1% increase in output variance considered.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195257","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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