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Volume 12
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Processes, Volume 12, Issue 9 (September 2024) – 21 articles

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14 pages, 3399 KiB  
Article
Metal-Nitrate-Catalyzed Levulinic Acid Esterification with Alkyl Alcohols: A Simple Route to Produce Bioadditives
by Márcio José da Silva and Mariana Teixeira Cordeiro
Processes 2024, 12(9), 1802; https://doi.org/10.3390/pr12091802 (registering DOI) - 24 Aug 2024
Abstract
This work developed an efficient route to produce fuel bioadditive alkyl levulinates. Special attention was paid to butyl levulinate, which is a bioadditive with an adequate carbon chain size to be blended with liquid fuels such as diesel or gasoline. In this process, [...] Read more.
This work developed an efficient route to produce fuel bioadditive alkyl levulinates. Special attention was paid to butyl levulinate, which is a bioadditive with an adequate carbon chain size to be blended with liquid fuels such as diesel or gasoline. In this process, levulinic acid was esterified with butyl alcohol using cheap and commercially affordable metal nitrates as catalysts, producing bioadditives at more competitive costs. Iron (III) nitrate was the most active and selective catalyst toward butyl levulinate among the salts evaluated. In solvent-free conditions, with a low molar ratio and catalyst load (1:6 acid to alcohol, 3 mol% of Fe (NO3)3), conversion and selectivity greater than 90% after an 8 h reaction was achieved. A comparison of the iron (III) nitrate with other metal salts demonstrated that its superior performance can be assigned to the highest Lewis acidity of Fe3+ cations. Measurements of pH allow the conclusion that a cation with high Lewis acidity led to a greater H+ release, which results in a higher conversion. Butyl levulinate and pseudobuty levulinate were always the primary and secondary products, respectively. The consecutive character of reactions between butyl alcohol and levulinic acid (formation of the pseudobutyl levulinate and its conversion to butyl levulinate) was verified by assessing the reactions at different temperatures and conversion rates. A variation in Fe(NO3)3 catalyst load impacted the conversion much more than reaction selectivity. The same effect was verified when the reactions were carried out at different temperatures. The reactivity of alcohols with different structures depended more on steric hindrance on the hydroxyl group than the size of the carbon chain. A positive aspect of this work is the use of a commercial iron nitrate salt as the catalyst, which has advantages over traditional mineral acids such as sulfuric and hydrochloric acids. This solid catalyst is not corrosive and avoids neutralization steps after reactions, minimizing the generation of residues and effluents. Full article
(This article belongs to the Special Issue 10th Anniversary of Processes: Design of the Chemical Industry of the Future)
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15 pages, 3546 KiB  
Article
Understanding the Catalytic Effect on the CO2 Regeneration Performance of Amine Aqueous Solutions
by Ke Li, Yuhang Shen, Teng Shen, Zhijun He, Rui Zhou, Zhouxiang Li, Youhong Xiao, Euiseok Hong and Haoran Yang
Processes 2024, 12(9), 1801; https://doi.org/10.3390/pr12091801 (registering DOI) - 24 Aug 2024
Abstract
To address the high energy consumption of the carbon capture and storage process in the shipping industry, the effects of several commonly used commercial catalysts, such as HZSM-5-25, γ-Al2O3, and SiO2, as well as a self-prepared catalyst, [...] Read more.
To address the high energy consumption of the carbon capture and storage process in the shipping industry, the effects of several commonly used commercial catalysts, such as HZSM-5-25, γ-Al2O3, and SiO2, as well as a self-prepared catalyst, Zr-HZSM-5-25, on the regeneration of MEA solution and MEA + MDEA mixed solution were investigated in this paper. The results showed that Zr-HZSM-5-25 had the best catalytic effect on the regeneration process of the MEA aqueous solution, which could increase the instantaneous maximum CO2 regeneration rate of the MEA-rich solution by about 8.25% and the average regeneration rate by about 5.28%. For the MEA + MDEA mixed solution, the reaction between tertiary amine MDEA and CO2 produced a large amount of HCO3− in the reaction system, which could accelerate the release of CO2 to a large extent, which resulted in the catalytic effect of the Zr-HZSM-5-25 catalyst on the regeneration process of the mixed amine solution being significantly lower than that on the single MEA solution, with an increase of 4.91% in the instantaneous maximum regeneration rate. This instantaneous maximum regeneration rate was only increased by 4.91%. While Zr-HZSM-5-25 showed a better performance in the bench-scale test, it reduced CO2 regeneration energy consumption by 7.3%. Full article
(This article belongs to the Topic Carbon Capture, Storage and Utilisation Technologies (CCS/CCU) - 2nd Volume)
13 pages, 446 KiB  
Review
Ohmic Heating in Food Processing: An Overview of Plant-Based Protein Modification
by Israel Felipe dos Santos, Tatiana Colombo Pimentel, Adriano Gomes da Cruz, Paulo César Stringheta, Evandro Martins and Pedro Henrique Campelo
Processes 2024, 12(9), 1800; https://doi.org/10.3390/pr12091800 (registering DOI) - 24 Aug 2024
Abstract
This review provides an analysis of ohmic heating in food processing and its effect on plant proteins. This study explores the effect of this technology on protein denaturation and aggregation, affecting both non-covalent and covalent bonds. These structural and chemical changes have significant [...] Read more.
This review provides an analysis of ohmic heating in food processing and its effect on plant proteins. This study explores the effect of this technology on protein denaturation and aggregation, affecting both non-covalent and covalent bonds. These structural and chemical changes have significant implications for the techno-functional properties of proteins, contributing to their use in food processing. This article emphasizes the need to adjust processing conditions to maximize the benefits of ohmic heating, distinguishing it from other traditional thermal techniques due to its direct and controllable impact. By highlighting these contributions, this review serves as a resource for researchers and professionals interested in innovation and efficiency in food processing through the use of emerging technologies. Full article
(This article belongs to the Special Issue Feature Papers in the "Food Process Engineering" Section)
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22 pages, 10121 KiB  
Review
Recent Advances in the Photocatalytic Degradation of Phenol over Bi-Based Oxide Catalysts
by Zhangpei Liu, Maosheng Qian, Xiaomeng Cheng and Zhiming Liu
Processes 2024, 12(9), 1799; https://doi.org/10.3390/pr12091799 (registering DOI) - 24 Aug 2024
Abstract
Wastewater containing phenolic organic compounds, such as phenol, produced during industrial manufacturing processes, poses a significant threat to aquatic ecosystems and crops. Photocatalytic technology is considered the most promising approach to water treatment due to its efficiency and eco-friendly advantages. Compared to other [...] Read more.
Wastewater containing phenolic organic compounds, such as phenol, produced during industrial manufacturing processes, poses a significant threat to aquatic ecosystems and crops. Photocatalytic technology is considered the most promising approach to water treatment due to its efficiency and eco-friendly advantages. Compared to other photocatalysts, Bi-based oxides are more efficient due to their unique layered structure, which allows for photocatalytic reactions to occur between layers. This review introduces the synthesis methods of various bismuth-based multi-element oxides and their efficiency in the photocatalytic decomposition of phenol. The effects of elemental doping, defect introduction, and heterojunction construction on the catalytic performance and structure of Bi-based oxides are discussed. The mechanisms for the photocatalytic degradation of phenol over different materials are also summarized and discussed. Full article
(This article belongs to the Special Issue Municipal Wastewater Treatment and Removal of Micropollutants)
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12 pages, 2108 KiB  
Article
Study on Ship Exhaust Gas Denitrification Technology Based on Vapor-Phase Oxidation and Liquid-Phase Impingement Absorption
by Yuanqing Wang and Wenyao Ma
Processes 2024, 12(9), 1798; https://doi.org/10.3390/pr12091798 (registering DOI) - 24 Aug 2024
Abstract
A system combining gas-phase oxidation and liquid-phase collision absorption for removing NO from marine diesel engine exhaust was proposed. This method was the first to utilize different physical states of the same mixed solution to achieve both pre-oxidation and impingement reduction absorption of [...] Read more.
A system combining gas-phase oxidation and liquid-phase collision absorption for removing NO from marine diesel engine exhaust was proposed. This method was the first to utilize different physical states of the same mixed solution to achieve both pre-oxidation and impingement reduction absorption of exhaust gases. During the pre-oxidation stage, a mixture of (NH4)2S2O8 and urea solution was atomized into a spray using an ultrasonic nebulizer to increase the contact area between the oxidant and the exhaust gas, thereby efficiently pre-oxidizing the exhaust gas in the gas phase. In the liquid-phase absorption stage, the (NH4)2S2O8 and urea solution was used in an impingement absorption process, which not only enhanced gas–liquid mass transfer efficiency but also effectively inhibited the formation of nitrates. Experimental results showed that, without increasing the amount of absorbent used, the maximum NO removal efficiency of this method reached 97% (temperature, 343 K; (NH4)2S2O8 concentration, 0.1 mol/L; urea concentration, 1.5 mol/L; NO concentration, 1000 ppm; pH, 7; impinging stream velocity, 15 m/s), compared to 72% using the conventional liquid-phase oxidation absorption method. Additionally, this method required only the addition of a nebulizer and two opposing nozzles to the existing desulfurization tower to achieve simultaneous removal of sulfur and nitrogen oxides from the exhaust gas, with low retrofitting costs making it favorable for practical engineering applications. Full article
(This article belongs to the Section Sustainable Processes)
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22 pages, 5160 KiB  
Article
Comparison of Advanced Multivariable Control Techniques for Axial-Piston Pump
by Alexander Mitov, Tsonyo Slavov and Jordan Kralev
Processes 2024, 12(9), 1797; https://doi.org/10.3390/pr12091797 - 23 Aug 2024
Viewed by 221
Abstract
This article is devoted to a comparison of two advanced control techniques applied to the same plant. The plant is a certain type of axial-piston pump. A linear-quadratic (LQR) controller and an H-infinity (H∞) controller were synthesized to regulate the displacement [...] Read more.
This article is devoted to a comparison of two advanced control techniques applied to the same plant. The plant is a certain type of axial-piston pump. A linear-quadratic (LQR) controller and an H-infinity (H∞) controller were synthesized to regulate the displacement volume of the pump. The classical solution to such a problem is to use a hydro-mechanical controller (by pressure, flow rate, or power) but, in the available sources, there are solutions that implement proportional-integral-derivative (PID), LQR, model predictive control (MPC), etc. Unlike a classical solution, in our case, the hydro-mechanical controller is replaced by an electro-hydraulic proportional valve, which receives a reference signal from an industrial microcontroller. It is used as the actuator of the pump swash plate. The pump swash plate swivel angle determines the displacement volume, respectively, and the flow rate of the pump. The microcontroller is capable of embedding various control algorithms with different structures and complexities. The developed LQR and H∞ controllers are compared in the simulation and real experiment conditions. For this purpose, the authors have developed a laboratory experimental test bench, enabling a real-time function of the control system via USB/CAN communication. Both controllers are compared under different pump loading modes. Also, this paper contributes an uncertain model of an axial-piston pump with proportional valve control that is obtained from experimental data. Based on this model, the robust stability of the closed-loop system is investigated by comparing the structure of a singular value (μ). The investigations show that both control systems achieved robust stability. Moreover, they can tolerate up to four times larger uncertainties than modeled ones. The system with the H∞ controller attenuates approximately at least 30 times the disturbances with frequency up to 1 rad/s while the system with the LQR controller attenuates at least 10 times the same disturbances. Full article
(This article belongs to the Special Issue 10th Anniversary of Processes—Recent Advances in Process Control and Monitoring)
18 pages, 2898 KiB  
Article
Study on Short-Term Electricity Load Forecasting Based on the Modified Simplex Approach Sparrow Search Algorithm Mixed with a Bidirectional Long- and Short-Term Memory Network
by Chenjun Zhang, Fuqian Zhang, Fuyang Gou and Wensi Cao
Processes 2024, 12(9), 1796; https://doi.org/10.3390/pr12091796 - 23 Aug 2024
Viewed by 230
Abstract
In order to balance power supply and demand, which is crucial for the safe and effective functioning of power systems, short-term power load forecasting is a crucial component of power system planning and operation. This paper aims to address the issue of low [...] Read more.
In order to balance power supply and demand, which is crucial for the safe and effective functioning of power systems, short-term power load forecasting is a crucial component of power system planning and operation. This paper aims to address the issue of low prediction accuracy resulting from power load volatility and nonlinearity. It suggests optimizing the number of hidden layer nodes, number of iterations, and learning rate of bi-directional long- and short-term memory networks using the improved sparrow search algorithm, and predicting the actual load data using the load prediction model. Using actual power load data from Wuxi, Jiangsu Province, China, as a dataset, the model makes predictions. The results indicate that the model is effective because the enhanced sparrow algorithm optimizes the bi-directional long- and short-term memory network model for predicting the power load data with a relative error of only 2%, which is higher than the prediction accuracy of the other models proposed in the paper. Full article
(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges)
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20 pages, 3998 KiB  
Article
PID Controller Design for an E. coli Fed-Batch Fermentation Process System Using Chaotic Electromagnetic Field Optimization
by Olympia Roeva, Tsonyo Slavov and Jordan Kralev
Processes 2024, 12(9), 1795; https://doi.org/10.3390/pr12091795 - 23 Aug 2024
Viewed by 190
Abstract
This paper presents an optimal tuning of a proportional integral differential (PID) controller used to maintain glucose concentration at a desired set point. The PID controller synthesizes an appropriate feed rate profile for an E. coli fed-batch cultivation process. Mathematical models are developed [...] Read more.
This paper presents an optimal tuning of a proportional integral differential (PID) controller used to maintain glucose concentration at a desired set point. The PID controller synthesizes an appropriate feed rate profile for an E. coli fed-batch cultivation process. Mathematical models are developed based on dynamic mass balance equations for biomass, substrate, and product concentration of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation for bacterial phytase extracellular production. For model parameter identification and PID tuning, a hybrid metaheuristic technique—chaotic electromagnetic field optimization (CEFO)—is proposed. In the hybridization, a chaotic map is used for the generation of a new electromagnetic particle instead of the electromagnetic field optimization (EFO) search strategy. The CEFO combines the exploitation capability of the EFO algorithm and the exploration power of ten different chaotic maps. The comparison of the results with classical EFO shows the superior behaviour of the designed CEFO. An improvement of 30% of the objective function is achieved by applying CEFO. Based on the obtained mathematical models, 10 PID controllers are tuned. The simulation experiments show that the designed controllers are robust, resulting in a good control system performance. The closed-loop transient responses for the corresponding controllers are similar to the estimated models. The settling time of the control system based on the third PID controller for all estimated models is approximately 9 min and the overshoot is approximately 15%. The proposed CEFO algorithm can be considered an effective methodology for mathematical modelling and achievement of high quality and better performance of the designed closed-loop system for cultivation processes. Full article
(This article belongs to the Special Issue Challenges and Advances of Process Control Systems)
21 pages, 883 KiB  
Article
Mimicking Marine Conditions to Improve Prodigiosin Yields in Bioreactor
by Ricardo F. S. Pereira and Carla C. C. R. de Carvalho
Processes 2024, 12(9), 1794; https://doi.org/10.3390/pr12091794 - 23 Aug 2024
Viewed by 191
Abstract
Prodigiosin is a red bacterial pigment with great potential as a natural dye and drug precursor, while presenting several pharmacological properties, including antimicrobial and anticancer activities. Its commercialization for biomedical applications, however, remains scarce. The major limitations are related to the lack of [...] Read more.
Prodigiosin is a red bacterial pigment with great potential as a natural dye and drug precursor, while presenting several pharmacological properties, including antimicrobial and anticancer activities. Its commercialization for biomedical applications, however, remains scarce. The major limitations are related to the lack of efficient bioprocesses and scaling up from laboratory to production. In the present work, the upstream process for prodigiosin production was developed using a marine Serratia rubidaea isolated from a sample collected near a shallow-water hydrothermal vent. The yield of product per biomass was found to be influenced by the cell concentration in the inoculum. The system was scaled up to 2 L stirred tank reactors with two different vessel geometries. It was shown that the vessel geometry and a cascade control mode for regulating the dissolved oxygen concentration influenced the volumetric oxygen mass transfer coefficient (kLa) and thus prodigiosin production. To improve product yields, strategies to mimic the aeration conditions found at the sampling site were tested. When the inoculum was grown for 5 h at 200 rpm and for 19 h at 25 rpm, which significantly decreased the oxygen available, the cells produced 588.2 mgproduct/gbiomass, corresponding to a production of 1066.2 mg of prodigiosin in 24 h and a productivity of 36.1 mgproduct/(L.h). This is a 3.7-fold increase in prodigiosin yield and a 4.5-fold increase in productivity in relation to when no particular strategy was promoted. Additionally, it was shown that lipid analysis and flow cytometry may be used as reliable at-line analytical tools, allowing the monitoring of cell condition and prodigiosin production during fermentation. Full article
(This article belongs to the Topic Bioreactors: Control, Optimization and Applications - 2nd Volume)
38 pages, 3326 KiB  
Review
Bio-Recovery of Metals through Biomining within Circularity-Based Solutions
by Petronela Cozma, Camelia Bețianu, Raluca-Maria Hlihor, Isabela Maria Simion and Maria Gavrilescu
Processes 2024, 12(9), 1793; https://doi.org/10.3390/pr12091793 - 23 Aug 2024
Viewed by 280
Abstract
Given the current highest demand in history for raw materials, there is a growing demand for the recovery of key metals from secondary sources, in order to prevent metal depletion and to reduce the risk of toxic discharges into the environment. This paper [...] Read more.
Given the current highest demand in history for raw materials, there is a growing demand for the recovery of key metals from secondary sources, in order to prevent metal depletion and to reduce the risk of toxic discharges into the environment. This paper focuses on the current nature-based solutions (i.e., biomining and bioleaching) applied to resource recovery (metals) from solid matrices. Biomining exploits the potential of microorganisms to facilitate the extraction and recovery of metals from a wide range of waste materials as an interesting alternative, replacing primary raw materials with secondary material resources (thus improving metal recycling rates in the context of the circular economy). Special attention was paid to the analysis of metal biomining from a process sustainability perspective. In this regard, several supporting tools (e.g., life cycle assessment, LCA), developed to assist decision-makers in the complex process of assessing and scaling-up remediation projects (including biomining), were discussed. The application of LCA in biomining is still evolving, and requires comprehensive case studies to improve the methodological approach. This review outlines the fact that few studies have focused on demonstrating the environmental performance of the biomining process. Also, further studies should be performed to promote the commercial opportunities of biomining, which can be used to recover and recycle metals from solid matrices and for site remediation. Despite some important disadvantages (poor process kinetics; metal toxicity), biomining is considered to be a cleaner approach than conventional mining processes. However, implementing it on a large scale requires improvements in regulatory issues and public acceptance. Full article
(This article belongs to the Special Issue Microbial Bioremediation of Environmental Pollution (2nd Edition))
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12 pages, 3929 KiB  
Article
Acid-Etched Fracture Conductivity with In Situ-Generated Acid in Ultra-Deep, High-Temperature Carbonate Reservoirs
by Haizheng Jia, Hongyuan Pu, Jianmin Li, Junchao Wang, Xi Chen, Jianye Mou and Budong Gao
Processes 2024, 12(9), 1792; https://doi.org/10.3390/pr12091792 - 23 Aug 2024
Viewed by 211
Abstract
In situ-generated acid is commonly employed in ultra-deep, high-temperature carbonate reservoirs during acid fracturing to increase the effective acid penetration distance. However, the variation pattern of acid-etched fracture conductivity with in situ-generated acid has not been systematically studied. This paper investigates the evolution [...] Read more.
In situ-generated acid is commonly employed in ultra-deep, high-temperature carbonate reservoirs during acid fracturing to increase the effective acid penetration distance. However, the variation pattern of acid-etched fracture conductivity with in situ-generated acid has not been systematically studied. This paper investigates the evolution of the conductivity of primary and secondary fractures through a series of experiments involving in situ acid displacement and acid-etched fracture conductivity measurement. Based on the experimental results, a calculation model for the conductivity of acid-etched fractures with in situ-generated acid was established. The study indicates that after acid etching, rough particulate points and grooved dissolution patterns form on the surfaces of primary and secondary fractures, respectively. The dissolution volume in primary fractures is greater than that in secondary fractures, with both showing a linear increase over time. Due to the presence of dissolution grooves on the surfaces of secondary fractures, their conductivity is higher than that of primary fractures under the same acid–rock contact time. The conductivity of both primary and secondary fractures increases with the acid–rock contact time. However, beyond approximately 70 min of contact time, the conductivity of primary fractures shows no significant increase. The conductivity of primary and secondary fractures with in situ-generated acid is slightly lower than that with gelled acid under the same contact time, but significantly higher than that with crosslinked acid. This study provides guidance for the design and parameter optimization of acid fracturing in ultra-deep, high-temperature carbonate reservoirs. Full article
(This article belongs to the Special Issue Phase Change, Interphase Coupling, and Multiphase Transport in Porous Structures)
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17 pages, 4972 KiB  
Article
Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage–Distribution Networks
by Lihua Zhong, Tong Ye, Yuyao Yang, Feng Pan, Lei Feng, Shuzhe Qi and Yuping Huang
Processes 2024, 12(9), 1791; https://doi.org/10.3390/pr12091791 - 23 Aug 2024
Viewed by 248
Abstract
As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance [...] Read more.
As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance operation and decision-making capability. An innovative dynamic carbon intensity calculation method is proposed, which more accurately calculates indirect carbon emissions of the power system through network topology in both spatial and temporal dimensions, thereby refining carbon responsibility allocation on the user side. Additionally, we integrate user-side SES and ladder-type carbon emission pricing into DN to create a low-carbon economic dispatch model. By framing the problem as a Markov decision process (MDP), we employ the DRL, specifically the deep deterministic policy gradient (DDPG) algorithm, enhanced with prioritized experience replay (PER) and orthogonal regularization (OR), to achieve both economic efficiency and environmental sustainability. The simulation results indicate that this method significantly reduces the operating costs and carbon emissions of DN. This study offers an innovative perspective on the synergistic optimization of SES with DN and provides a practical methodology for low-carbon economic dispatch in power systems. Full article
(This article belongs to the Special Issue Battery Management Processes, Modeling, and Optimization)
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15 pages, 5420 KiB  
Article
Hydrogen-Rich Syngas Production from Waste Textile Gasification Coupling with Catalytic Reforming under Steam Atmosphere
by Xinchao Zhuang, Nengwu Zhu, Fei Li, Haisheng Lin, Chao Liang, Zhi Dang and Yuquan Zou
Processes 2024, 12(9), 1790; https://doi.org/10.3390/pr12091790 - 23 Aug 2024
Viewed by 345
Abstract
The average annual global production of waste textiles exceeds 92 million tons, with the majority landfilled and incinerated, resulting in energy waste and environmental pollution. In this study, a thermal conversion process for waste textiles by gasification coupling with catalytic reforming under a [...] Read more.
The average annual global production of waste textiles exceeds 92 million tons, with the majority landfilled and incinerated, resulting in energy waste and environmental pollution. In this study, a thermal conversion process for waste textiles by gasification coupling with catalytic reforming under a steam atmosphere was proposed. The gasification performance of the waste textiles jumped with the introduction of steam and catalyst compared to pyrolysis at 800 °C. The syngas yield increased from 20.86 to 80.97 mmol/g and the hydrogen concentration increased from 17.79 to 50.91 vol.%, which was an increase of 288.12% and 186.18%, respectively. The excellent gasification performance mainly came from two sources: steam promotion for volatiles production and Fe-N-BC promotion for steam reforming of volatiles by Fe2O3, Fe3O4, Fe-Nx, etc. This study has achieved the efficient production of hydrogen-rich syngas from waste textiles, providing a new idea and theoretical basis for the effective removal and utilization of waste textiles. Full article
(This article belongs to the Section Catalysis Enhanced Processes)
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30 pages, 9217 KiB  
Review
Computational Analysis of Upscaled Fibrotic Liver Multi-Lobule Flows and Metabolism
by Dennis Coombe, Cooper Wallace, Vahid Rezania and Jack A. Tuszynski
Processes 2024, 12(9), 1789; https://doi.org/10.3390/pr12091789 - 23 Aug 2024
Viewed by 250
Abstract
The modeling of fibrotic effects on fluid flow and metabolism in the liver can be computationally challenging. This paper combines innovative concepts based on fundamental physics to address such issues at the level of the liver functional unit, the lobule, and upscales and [...] Read more.
The modeling of fibrotic effects on fluid flow and metabolism in the liver can be computationally challenging. This paper combines innovative concepts based on fundamental physics to address such issues at the level of the liver functional unit, the lobule, and upscales and extends this to a multi-lobule tissue scale analysis. Fibrosis effects on fluid flow and metabolism are introduced using percolation theory and its consequences are explored for single lobule and multi-lobule patterns, without and with distortion. Full article
(This article belongs to the Special Issue Multiscale Modeling and Control of Biomedical Systems)
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13 pages, 2422 KiB  
Article
Prediction of Liquid Accumulation Height in Gas Well Tubing Using Integration of Crayfish Optimization Algorithm and XGBoost
by Wenlong Xia, Botao Liu and Hua Xiang
Processes 2024, 12(9), 1788; https://doi.org/10.3390/pr12091788 - 23 Aug 2024
Viewed by 202
Abstract
The prediction of the liquid build-up height in gas wells is a crucial aspect of reservoir development and is essential for the efficient execution of drainage and gas extraction operations. Excessive liquid accumulation can lead to well flooding and operational shutdowns, resulting in [...] Read more.
The prediction of the liquid build-up height in gas wells is a crucial aspect of reservoir development and is essential for the efficient execution of drainage and gas extraction operations. Excessive liquid accumulation can lead to well flooding and operational shutdowns, resulting in significant economic losses. To prevent such occurrences, accurate estimation of the liquid height in gas well tubing is necessary. However, existing petroleum engineering models face numerous challenges in predicting liquid height, including complex theoretical solution steps and reliance on fundamental well parameters and extensive empirical data. The paper proposes an innovative blend of the Crayfish Optimization Algorithm (COA) with the eXtreme Gradient Boosting (XGBoost) methodology to forecast the liquid loading heights in gas wells. The COA is employed to optimize eight hyperparameters of the XGBoost, including the number of trees, maximum depth, minimum child weight, learning rate, minimum loss reduction, subsample, L1 regularization, and L2 regularization. After fine-tuning the hyperparameters, the XGBoost undergoes a retraining process, followed by an evaluation. Through comparative analysis with actual measurements from 32 wells in a gas field as well as support vector regression (SVR), XGBoost, random forest (RF), and PLATA (which predict liquid volume in the tubing and annulus), the proposed COA–XGBoost demonstrates a high degree of alignment with the measured values. It provides the most accurate predictions, with a mean relative error of only 2.25%. Compared with the traditional XGBoost, the COA–XGBoost reduced the mean relative error in predicting gas well tubing liquid loading height by 32.63%. Compared with the previous PLATA, the proposed model achieved a 3.52% decrease in mean relative error, enabling more accurate assessment of the severity of liquid loading in gas wells. Full article
(This article belongs to the Section Energy Systems)
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15 pages, 4964 KiB  
Article
Adaptive Finite-Time Constrained Attitude Stabilization for an Unmanned Helicopter System under Input Delay and Saturation
by Yang Li and Ting Yang
Processes 2024, 12(9), 1787; https://doi.org/10.3390/pr12091787 - 23 Aug 2024
Viewed by 193
Abstract
This study focuses on addressing the constrained attitude stabilization problem for an unmanned helicopter (UH) system subject to disturbances, input delay and actuator saturation. A constrained memory sliding mode is first presented to constrain the flight attitude while handling the input delay. On [...] Read more.
This study focuses on addressing the constrained attitude stabilization problem for an unmanned helicopter (UH) system subject to disturbances, input delay and actuator saturation. A constrained memory sliding mode is first presented to constrain the flight attitude while handling the input delay. On this basis, an adaptive finite-time nonlinear observer is proposed to estimate the lumped disturbance with unknown upper bound. Moreover, based on the hyperbolic tangent function, a saturated attitude controller is designed to tackle the input saturation problem via the adaptive laws. The finite-time stability of the closed-loop constrained attitude system is proved by Lyapunov synthesis. Finally, the developed scheme can accomplish attitude stabilization and overcome the influence of disturbances, attitude constraint, input delay and actuator saturation in an easy way. Numerical simulations are carried out to demonstrate the effectiveness of the proposed control scheme. Full article
(This article belongs to the Section Automation Control Systems)
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15 pages, 2205 KiB  
Article
Prediction Model-Assisted Optimization Scheduling Strategy for Renewable Energy in the Microgrid
by Xiaoqing Cao, Xuan Yang, Lin Li, Lunjia Shen, Wenjie Ma, Rongxin Yang and Hongbo Zou
Processes 2024, 12(9), 1786; https://doi.org/10.3390/pr12091786 - 23 Aug 2024
Viewed by 279
Abstract
As the global reliance on renewable energy sources grows, wind and photovoltaic power, as pivotal components, pose significant challenges to power system dispatch due to their volatility and uncertainty. To effectively address this challenge, this paper proposes a renewable energy optimization dispatch strategy [...] Read more.
As the global reliance on renewable energy sources grows, wind and photovoltaic power, as pivotal components, pose significant challenges to power system dispatch due to their volatility and uncertainty. To effectively address this challenge, this paper proposes a renewable energy optimization dispatch strategy based on a prediction model. First, this paper constructs a prediction model combining functional data analysis and recurrent neural networks (RNNs) to achieve an accurate prediction of renewable energy output. On this basis, considering the economic and environmental benefits of system operation, an optimal multi-objective dispatch model for renewable energy is established, and the multi-objective optimization problem is transformed into a single-objective optimization problem using weighting methods to reduce the complexity of the solution. Finally, a typical microgrid test system is used to verify the effectiveness and feasibility of the proposed method. The results of the numerical example show that the proposed model can achieve an accurate prediction of renewable energy sources, reduce the conservatism of traditional dispatch decisions, and balance economic and environmental benefits. Full article
(This article belongs to the Section Energy Systems)
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14 pages, 3328 KiB  
Article
A Novel Chaotic Particle Swarm-Optimized Backpropagation Neural Network PID Controller for Indoor Carbon Dioxide Control
by Suli Zhang, Hui Li and Yiting Chang
Processes 2024, 12(9), 1785; https://doi.org/10.3390/pr12091785 - 23 Aug 2024
Viewed by 233
Abstract
In the continuously evolving landscape of novel smart control strategies, optimization techniques play a crucial role in achieving precise control of indoor air quality. This study aims to enhance indoor air quality by precisely regulating carbon dioxide (CO2) levels through an [...] Read more.
In the continuously evolving landscape of novel smart control strategies, optimization techniques play a crucial role in achieving precise control of indoor air quality. This study aims to enhance indoor air quality by precisely regulating carbon dioxide (CO2) levels through an optimized control system. Prioritizing fast response, short settling time, and minimal overshoot is essential to ensure accurate control. To achieve this goal, chaos optimization is applied. By using the global search capability of the chaos particle swarm optimization (CPSO) algorithm, the initial weights connecting the input layer to the hidden layer and the hidden layer to the output layer of the backpropagation neural network (BPNN) are continuously optimized. The optimized weights are then applied to the BPNN, which employs its self-learning capability to calculate the output error of each neuronal layer, progressing from the output layer backward. Based on these errors, the weights are adjusted accordingly, ultimately tuning the proportional–integral–derivative (PID) controller to its optimal parameters. When comparing simulation results, it is evident that, compared to the baseline method, the enhanced Chaos Particle Swarm Optimization Backpropagation Neural Network PID (CPSO-BPNN-PID) controller proposed in this study exhibits the shortest settling time, approximately 0.125 s, with a peak value of 1, a peak time of 0.2 s, and zero overshoot, demonstrating exceptional control performance. The novelty of this control algorithm lies in the integration of four distinct technologies—chaos optimization, particle swarm optimization (PSO), BPNN, and PID controller—into a novel controller for precise regulation of indoor CO2 concentration. Full article
(This article belongs to the Section Automation Control Systems)
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17 pages, 1557 KiB  
Article
Strategy for Renewable Energy Consumption Based on Scenario Reduction and Flexible Resource Utilization
by Xiaoqing Cao, Xuan Yang, He Li, Di Chen, Zhengyu Zhang, Qingrui Yang and Hongbo Zou
Processes 2024, 12(9), 1784; https://doi.org/10.3390/pr12091784 - 23 Aug 2024
Viewed by 270
Abstract
With the growing global emphasis on renewable energy, the issue of renewable energy consumption has emerged as a hot topic of current research. In response to the volatility and uncertainty in the process of renewable energy consumption, this study proposes a renewable energy [...] Read more.
With the growing global emphasis on renewable energy, the issue of renewable energy consumption has emerged as a hot topic of current research. In response to the volatility and uncertainty in the process of renewable energy consumption, this study proposes a renewable energy consumption strategy based on scenario reduction and flexible resource utilization. This strategy aims to achieve the efficient utilization of renewable energy sources through optimized resource allocation while ensuring the stable operation of the power system. Firstly, this study employs scenario analysis methods to model the volatility and uncertainty of renewable energy generation. By applying scenario reduction techniques, typical scenarios are selected to reduce the complexity of the problem, providing a foundation for the construction of the optimization model. At the same time, by fully considering the widely available small-capacity energy storage units within the system, a flexible cloud energy storage scheduling model is constructed to assist in renewable energy consumption. Finally, the validity and feasibility of the proposed method are demonstrated through case studies. Through analysis, the proposed scenario generation method achieved a maximum value of 26.28 for the indicator IDBI and a minimum value of 1.59 for the indicator ICHI. Based on this foundation, the cloud energy storage model can fully absorb renewable energy, reducing the net load peak-to-trough difference to 1759 kW, a decrease of 809 kW compared with the traditional model. Full article
(This article belongs to the Section Energy Systems)
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16 pages, 5075 KiB  
Article
Pour Point Prediction Method for Mixed Crude Oil Based on Ensemble Machine Learning Models
by Jimiao Duan, Zhi Kou, Huishu Liu, Keyu Lin, Sichen He and Shiming Chen
Processes 2024, 12(9), 1783; https://doi.org/10.3390/pr12091783 - 23 Aug 2024
Viewed by 273
Abstract
Pipelines are the most common way to transport crude oil. The crude oil developed from different fields is mixed first and then transported. The pour point of mixed crude oil is very important for pipeline schemes and ensuring the safe, efficient, and flexible [...] Read more.
Pipelines are the most common way to transport crude oil. The crude oil developed from different fields is mixed first and then transported. The pour point of mixed crude oil is very important for pipeline schemes and ensuring the safe, efficient, and flexible operation of the pipeline. An integrated machine learning model based on XGBoost is identified as optimal to predict the pour point of mixed crude oil by comprehensive comparison among six different types of machine learning models: multiple linear regression, random forest, support vector machine, LightGBM, backpropagation neural network, and XGBoost. A mixed crude oil pour point prediction model with strong engineering adaptability is proposed, focusing on enhancing the flexibility of machine learning model inputs (using density and viscosity instead of component crude oil pour points) and addressing challenges such as data volume and input missing in engineering scenarios. With the inputs of pour point Tg, density ρ, viscosity μ, and ratio Xi in component oils, the mean absolute error of the model prediction estimations after training with 8912 data is 1.12 °C, when the pour point Tg of the component crude oil is missing, the mean absolute error is 1.93 °C and the percentage of the predicted absolute error within 2 °C is 88.0%. This study can provide support for the intelligent control of flow properties of pipeline transport mixed oil. Full article
(This article belongs to the Section Energy Systems)
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15 pages, 3259 KiB  
Article
Reconstruction of the Municipal Wastewater-Treatment Plant According to the Principles of Aerobic Granular Sludge Cultivation
by Miroslav Hutňan, Barbora Jankovičová, Lenka Jajcaiová, Mikhael Sammarah, Karol Kratochvíl and Nikola Šoltýsová
Processes 2024, 12(9), 1782; https://doi.org/10.3390/pr12091782 - 23 Aug 2024
Viewed by 320
Abstract
The work presents the concept of aerobic granular sludge (AGS) and its potential for wastewater treatment. The work also evaluates the condition of the SBR (Sequencing Batch Reactor) type of municipal wastewater-treatment plant (WWTP) after its reconstruction into a system with AGS. The [...] Read more.
The work presents the concept of aerobic granular sludge (AGS) and its potential for wastewater treatment. The work also evaluates the condition of the SBR (Sequencing Batch Reactor) type of municipal wastewater-treatment plant (WWTP) after its reconstruction into a system with AGS. The WWTP parameters achieved before and after reconstruction were compared. Operational measurements of the process during the individual phases of the treatment process showed a balanced concentration profile of the monitored parameters in the span of the semicontinuous cycle. Laboratory tests showed that the sludge from the WWTP has nitrification and denitrification rates comparable to the rates achieved for flocculent sludge, and it is also comparable to the nitrification and denitrification rates of AGS with size of granules below 400 µm. Despite the fact that complete sludge granulation was not achieved, the results measured at the WWTP confirmed the advantages of the AGS concept. Neither anaerobic nor anoxic conditions were identified in the SBR during the individual phases of operation, yet high removal efficiencies of ammonia and nitrate nitrogen and orthophosphate phosphorus were achieved. The concentration of ammonia and nitrate nitrogen at the WWTP effluent was below 5 mg/L, and the concentration of phosphorus was below 0.5 mg/L. Full article
(This article belongs to the Special Issue Municipal Wastewater Treatment and Removal of Micropollutants)
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