Journal Description
Processes
Processes
is an international, peer-reviewed, open access journal on processes/systems in chemistry, biology, material, energy, environment, food, pharmaceutical, manufacturing, automation control, catalysis, separation, particle and allied engineering fields published monthly online by MDPI. The Systems and Control Division of the Canadian Society for Chemical Engineering (CSChE S&C Division) and the Brazilian Association of Chemical Engineering (ABEQ) are affiliated with Processes and their members receive discounts on the article processing charges. Please visit Society Collaborations for more details.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, Inspec, AGRIS, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Chemical) / CiteScore - Q2 (Chemical Engineering (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 13.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.5 (2022);
5-Year Impact Factor:
3.4 (2022)
Latest Articles
An Online Energy-Saving Control Allocation Strategy Based on Self-Updating Loss Estimation for Multi-Motor Drive Systems
Processes 2024, 12(6), 1072; https://doi.org/10.3390/pr12061072 - 23 May 2024
Abstract
In this paper, an online energy-saving control allocation strategy based on self-updating loss estimation for multi-motor drive systems is proposed, where the impact of variations in motor parameters and distribution coefficients is considered. Firstly, a drive system model for multi-motor drive systems incorporating
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In this paper, an online energy-saving control allocation strategy based on self-updating loss estimation for multi-motor drive systems is proposed, where the impact of variations in motor parameters and distribution coefficients is considered. Firstly, a drive system model for multi-motor drive systems incorporating iron loss in permanent magnet synchronous motor (PMSM) is established. Then, a self-updating PMSM loss estimation method based on dynamic torque–current mapping is proposed. The torque–current mapping is initially identified based on the conv-fusion curve, and iteratively updated by optimal estimation. Subsequently, an online control allocation method based on line search is proposed, which mitigates the adverse effects caused by variations in distribution coefficients and reduces the total motor loss. Finally, the effectiveness of the proposed strategy is verified on the hardware-in-the-loop (HIL)-based platform. The results demonstrate that the strategy effectively enhances energy efficiency while maintaining the original control performance of the system.
Full article
(This article belongs to the Topic Energy Management and Efficiency in Electric Motors, Drives, Power Converters and Related Systems)
Open AccessArticle
Enzymic Deactivation in Tender Coconut Water by Supercritical Carbon Dioxide
by
Alice Zinneck Poça D’Água, Priscila Alves da Silva, Alessandra Lopes de Oliveira and Rodrigo Rodrigues Petrus
Processes 2024, 12(6), 1071; https://doi.org/10.3390/pr12061071 - 23 May 2024
Abstract
Polyphenol oxidase (PPO) and peroxidase (POD) are target enzymes in the processing of tender coconut water (TCW). This study primarily evaluated the combined effect of supercritical carbon dioxide (SC-CO2) and mild temperatures on the PPO and POD deactivation of TCW. A factorial design
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Polyphenol oxidase (PPO) and peroxidase (POD) are target enzymes in the processing of tender coconut water (TCW). This study primarily evaluated the combined effect of supercritical carbon dioxide (SC-CO2) and mild temperatures on the PPO and POD deactivation of TCW. A factorial design was performed to investigate the effect of temperature (in the range of 35 to 85 °C), pressure (75 to 370 bar), and holding time (13 to 47 min) on the enzymic deactivation, physicochemical parameters, and color of the TCW. The percentages of reduction in PPO activity ranged from 3.7 to 100%, and POD ranged from 43.4 to 100%. The pH values of the freshly extracted and processed TCW were 5.09 and 4.90, and the soluble solids content were 5.5 and 5.4 °Brix, respectively. The holding time (t) had a significant effect (p ≤ 0.1) on the total color variation. As for the reduction of PPO activity, the temperature (T) and the interaction between pressure (P) and t had a significant effect. None of variables (P, T, or t) affected (p > 0.1) the POD reduction, pH, and soluble solids variation. The combination of SC-CO2 and mild temperatures is a promising intervention in the enzymic stabilization of TCW.
Full article
(This article belongs to the Special Issue Non-thermal Technologies in Food Science, Volume II)
Open AccessArticle
CODAS–Hamming–Mahalanobis Method for Hierarchizing Green Energy Indicators and a Linearity Factor for Relevant Factors’ Prediction through Enterprises’ Opinions
by
Georgina Elizabeth Riosvelasco-Monroy, Iván Juan Carlos Pérez-Olguín, Salvador Noriega-Morales, Luis Asunción Pérez-Domínguez, Luis Carlos Méndez-González and Luis Alberto Rodríguez-Picón
Processes 2024, 12(6), 1070; https://doi.org/10.3390/pr12061070 - 23 May 2024
Abstract
As enterprises look forward to new market share and supply chain opportunities, innovative strategies and sustainable manufacturing play important roles for micro-, small, and mid-sized enterprises worldwide. Sustainable manufacturing is one of the practices aimed towards deploying green energy initiatives to ease climate
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As enterprises look forward to new market share and supply chain opportunities, innovative strategies and sustainable manufacturing play important roles for micro-, small, and mid-sized enterprises worldwide. Sustainable manufacturing is one of the practices aimed towards deploying green energy initiatives to ease climate change, presenting three main pillars—economic, social, and environmental. The issue of how to reach sustainability goals within the sustainable manufacturing of pillars is a less-researched area. This paper’s main purpose and novelty is two-fold. First, it aims to provide a hierarchy of the green energy indicators and their measurements through a multi-criteria decision-making point of view to implement them as an alliance strategy towards sustainable manufacturing. Moreover, we aim to provide researchers and practitioners with a forecasting method to re-prioritize green energy indicators through a linearity factor model. The CODAS–Hamming–Mahalanobis method is used to obtain preference scores and rankings from a 50-item list. The resulting top 10 list shows that enterprises defined nine items within the economic pillar as more important and one item on the environmental pillar; items from the social pillar were less important. The implication for MSMEs within the manufacturing sector represents an opportunity to work with decision makers to deploy specific initiatives towards sustainable manufacturing, focused on profit and welfare while taking care of natural resources. In addition, we propose a continuous predictive analysis method, the linearity factor model, as a tool for new enterprises to seek a green energy hierarchy according to their individual needs. The resulting hierarchy using the predictive analysis model presented changes in the items’ order, but it remained within the same two sustainable manufacturing pillars: economic and environmental.
Full article
(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)
Open AccessArticle
An Application of Lean Techniques to Construct an Integrated Management Systems Preventive Action Model and Evaluation: Kaizen Projects
by
Matshidiso Moso and Oludolapo Akanni Olanrewaju
Processes 2024, 12(6), 1069; https://doi.org/10.3390/pr12061069 - 23 May 2024
Abstract
The Occupational Health and Safety system enforces the continual improvement culture in industries for much safer processes and zero injuries. The Quality Management System also enforces the same philosophy of continual improvement within the processing system for zero defects, hence a high productivity
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The Occupational Health and Safety system enforces the continual improvement culture in industries for much safer processes and zero injuries. The Quality Management System also enforces the same philosophy of continual improvement within the processing system for zero defects, hence a high productivity rate. Good quality products always result from good Overall Equipment Effectiveness; hence, Process Re-Engineering is essential for the good functioning of machinery. This research is based on Integrated Management System requirements in terms of problem-solving, especially the opportunities that arise within Quality nonconformances, Safety Incidents, as well as Process Engineering related breakdowns. This study aims to develop a troubleshooting system that evaluates continual improvement projects. The method used to develop the troubleshooting system is based on Total Quality Management, where lean principles are combined with kaizen concepts and quality standards. The proposed troubleshooting system is separated into three development phases: the first phase is for recording the details of the fault that has been raised, where one will record full details of the nonconformance, the time and date, validation of the nonconformance by the lab test or any other form of validation depending on the nature of the problem as well as the details of the location of the problem. The second phase is for problem classification, whether it is a quality nonconformance, Safety incident, or engineering-related breakdown. The deeper root cause analysis is performed by an application of lean techniques, which are the eight types of waste, Five Whys and Ishikawa analysis. The eight types of waste identify the type of waste contributed by the problem, the Five Whys analysis assists in finding the reason for the problem occurrence, and the Ishikawa analysis classifies the problem accordingly, which assists the analyst in identifying the area to focus on for problem-solving. The third phase is for a database system and an application of the kaizen philosophy by evaluating continual improvement projects as well as status reports on the permanent solutions to the faults. The proposed troubleshooting model was applied in a case study company to upgrade the problem-solving model that the company was using which was assisting for corrective and preventive action. The study resulted in drastic improvements; hence, continual improvement projects were evaluated within the problem occurrences.
Full article
(This article belongs to the Special Issue Challenges and Advances of Process Control Systems)
Open AccessArticle
A Study on the Man-Hour Prediction in Structural Steel Fabrication
by
Zhangliang Wei, Zhigang Li, Renzhong Niu, Peilin Jin and Zipeng Yu
Processes 2024, 12(6), 1068; https://doi.org/10.3390/pr12061068 - 23 May 2024
Abstract
Longitudinal cutting is the most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules. However, currently, the man-hours in factories are mainly estimated by experts, and the accuracy of this
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Longitudinal cutting is the most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules. However, currently, the man-hours in factories are mainly estimated by experts, and the accuracy of this method is relatively low. In this study, we propose a system that predicts man-hours with history data in the manufacturing process and that can be applied in practical structural steel fabrication. The system addresses the data inconsistency problem by one-hot encoding and data normalization techniques, Pearson correlation coefficient for feature selection, and the Random Forest Regression (RFR) for prediction. Compared with the other three Machine-Learning (ML) algorithms, the Random Forest algorithm has the best performance. The results demonstrate that the proposed system outperforms the conventional approach and has better forecast accuracy so it is suitable for man-hours prediction.
Full article
(This article belongs to the Section Materials Processes)
Open AccessArticle
Enhancement of Mine Images through Reflectance Estimation of V Channel Using Retinex Theory
by
Changlin Wu, Dandan Wang, Kaifeng Huang and Long Wu
Processes 2024, 12(6), 1067; https://doi.org/10.3390/pr12061067 - 23 May 2024
Abstract
The dim lighting and excessive dust in underground mines often result in uneven illumination, blurriness, and loss of detail in surveillance images, which hinders subsequent intelligent image recognition. To address the limitations of the existing image enhancement algorithms in terms of generalization and
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The dim lighting and excessive dust in underground mines often result in uneven illumination, blurriness, and loss of detail in surveillance images, which hinders subsequent intelligent image recognition. To address the limitations of the existing image enhancement algorithms in terms of generalization and accuracy, this paper proposes an unsupervised method for enhancing mine images in the hue–saturation–value (HSV) color space. Inspired by the HSV color space, the method first converts RGB images to the HSV space and integrates Retinex theory into the brightness (V channel). Additionally, a random perturbation technique is designed for the brightness. Within the same scene, a U-Net-based reflectance estimation network is constructed by enforcing consistency between the original reflectance and the perturbed reflectance, incorporating ResNeSt blocks and a multi-scale channel pixel attention module to improve accuracy. Finally, an enhanced image is obtained by recombining the original hue (H channel), brightness, and saturation (S channel), and converting back to the RGB space. Importantly, this image enhancement algorithm does not require any normally illuminated images during training. Extensive experiments demonstrated that the proposed method outperformed most existing unsupervised low-light image enhancement methods, qualitatively and quantitatively, achieving a competitive performance comparable to many supervised methods. Specifically, our method achieved the highest PSNR value of 22.18, indicating significant improvements compared to the other methods, and surpassing the second-best WCDM method by 10.3%. In terms of SSIM, our method also performed exceptionally well, achieving a value of 0.807, surpassing all other methods, and improving upon the second-place WCDM method by 19.5%. These results demonstrate that our proposed method significantly enhanced image quality and similarity, far exceeding the performance of the other algorithms.
Full article
(This article belongs to the Topic Green Mining, 2nd Volume)
Open AccessArticle
Validation of Fluid Flow Speed Behavior in Capillary Microchannels Using Additive Manufacturing (SLA Technology)
by
Victor H. Cabrera-Moreta, Jasmina Casals-Terré and Erick Salguero
Processes 2024, 12(6), 1066; https://doi.org/10.3390/pr12061066 - 23 May 2024
Abstract
This research explores fluid flow speed behavior in capillary channels using additive manufacturing, focusing on stereolithography (SLA). It aims to validate microchannels fabricated through SLA for desired fluid flow characteristics, particularly capillary-driven flow. The methodology involves designing, fabricating, and characterizing microchannels via SLA,
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This research explores fluid flow speed behavior in capillary channels using additive manufacturing, focusing on stereolithography (SLA). It aims to validate microchannels fabricated through SLA for desired fluid flow characteristics, particularly capillary-driven flow. The methodology involves designing, fabricating, and characterizing microchannels via SLA, with improvements such as an air-cleaning step facilitating the production of microchannels ranging from 300 to 1000 µ . Experimental validation assesses fluid flow speed behavior across channels of varying dimensions, evaluating the impact of channel geometry, surface roughness, and manufacturing parameters. The findings affirm the feasibility and efficacy of SLA in producing microchannels with consistent and predictable fluid flow behavior between 300 to 800 µ . This study contributes insights into microfluidic device fabrication techniques and enhances the understanding of fluid dynamics in capillary-driven systems. Overall, it underscores the potential of additive manufacturing, specifically SLA, in offering cost-effective and scalable solutions for microfluidic applications. The validated fluid flow speed behavior in capillary channels suggests new avenues for developing innovative microfluidic devices with improved performance and functionality, marking a significant advancement in the field.
Full article
(This article belongs to the Special Issue Micro/Nano Manufacturing Processes: Theories and Optimization Techniques)
Open AccessArticle
Experimental Study on Microwave Pyrolysis of Decommissioned Wind Turbine Blades Based on Silicon Carbide Absorbents
by
Dongwang Zhang, Qiang Song, Bo Hou, Man Zhang, Da Teng, Yaning Zhang, Rushan Bie and Hairui Yang
Processes 2024, 12(6), 1065; https://doi.org/10.3390/pr12061065 - 23 May 2024
Abstract
The rapid expansion of the scale of wind power has led to a wave of efforts to decommission wind turbine blades. The pyrolysis of decommissioned wind turbine blades (DWTBs) is a promising technological solution. Microwave pyrolysis offers the benefits of fast heating rates
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The rapid expansion of the scale of wind power has led to a wave of efforts to decommission wind turbine blades. The pyrolysis of decommissioned wind turbine blades (DWTBs) is a promising technological solution. Microwave pyrolysis offers the benefits of fast heating rates and uniform heat transfer, making it a widely used method in various heating applications. However, there are few studies on the microwave pyrolysis of DWTBs, and pyrolysis characteristics under different boundary conditions remain unclear. In this paper, we investigate the pyrolysis characteristics of DWTBs by utilizing silicon carbide (SiC) particles as a microwave absorbent. The results demonstrated that, when the microwave heating power increased from 400 W to 600 W, the heating rate and pyrolysis final temperature of the material increased, resulting in a reduction in pyrolysis residual solid yield from 88.30% to 84.40%. At 600 W, pyrolysis gas components included C2H4, CH4, and CO, while the tar components included phenol and toluene. The highest degree of pyrolysis was achieved under the condition of an SiC particle size of 0.85 mm, with better heating performance, and the calorific value of the pyrolysis gas generated was 36.95 MJ/Nm3. The DWTBs did not undergo pyrolysis when SiC was not added. However, when the mass ratio of SiC to DWTBs was 4, the tar yield was 4.7% and the pyrolysis gas yield was 17.0%, resulting in a faster heating rate and the highest degree of pyrolysis. Based on this, an optimal process for the microwave pyrolysis of DWTBs was proposed, providing a reference for its industrial application.
Full article
(This article belongs to the Special Issue Advances in Value-Added Products from Waste)
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Open AccessArticle
Engineering Implementation of the Acosta Fermentation Method to Obtain Cuban Schnapps with Reduced Concentrations of Higher Alcohols
by
Ariel Alain Vergel-Alfonso, Delvis Rafael Acosta-Martínez, José Ariel Arencibia-Sánchez, Francisco Rodríguez-Félix, Yosviel Reyes-Delgado, Rosa Virginia González-Morales, Rosbel Benítez-Sánchez, Ana Liz Gonzalez-Bravo and José Agustín Tapia-Hernández
Processes 2024, 12(6), 1064; https://doi.org/10.3390/pr12061064 - 22 May 2024
Abstract
The Acosta method involves rewiring the yeast metabolic pathway to enhance biomass production and prevent a significant increase in higher alcohols during the late stages of fermentation. This study aimed to assess fermentation conditions to achieve Cuban schnapps with reduced concentrations of higher
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The Acosta method involves rewiring the yeast metabolic pathway to enhance biomass production and prevent a significant increase in higher alcohols during the late stages of fermentation. This study aimed to assess fermentation conditions to achieve Cuban schnapps with reduced concentrations of higher alcohols and replicate the process on an industrial scale. To achieve this, the quality of final sugarcane molasses for fermentation by Saccharomyces cerevisiae (S. cerevisiae) yeast was evaluated. Industrial pre-fermentation and fermentation processes were successfully conducted, followed by laboratory-scale fermentation using the Acosta fermentation method to determine crucial parameters for industrial implementation. Operational parameters for fermentation were identified from the following results: 13.5 °Brix seeding, metabolic pathway inversion of S. cerevisiae at 16 h, and an air concentration of 0.1 m3/min. The resulting Cuban schnapps obtained using this method exhibited a concentration of higher alcohols of 132.5 mg/L, a value that is within the standard parameters, showing a positive impact of this fermentation method on the quality of the schnapps. Scaling up this method to an industrial level, in addition to offering higher quality products and being an economically viable alternative, also stands out for its sustainable and environmentally friendly aspect, and results in higher production of yeast biomass as a byproduct, which can be used for various purposes, such as animal feed. This method constitutes an important update to the schnapps production process as a technological improvement that respects sustainable production trends and the characteristics of the final product.
Full article
(This article belongs to the Special Issue Research and Optimization of Food Processing Technology)
Open AccessArticle
A Gated Recurrent Unit Model with Fibonacci Attenuation Particle Swarm Optimization for Carbon Emission Prediction
by
Jia Guo, Jiacheng Li, Yuji Sato and Zhou Yan
Processes 2024, 12(6), 1063; https://doi.org/10.3390/pr12061063 - 22 May 2024
Abstract
Predicting carbon emissions is important in various sectors, including environmental management, economic planning, and energy policy. Traditional forecasting models typically require extensive training data to achieve high accuracy. However, carbon emission data are usually available on an annual basis, which is insufficient for
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Predicting carbon emissions is important in various sectors, including environmental management, economic planning, and energy policy. Traditional forecasting models typically require extensive training data to achieve high accuracy. However, carbon emission data are usually available on an annual basis, which is insufficient for effectively training conventional forecasting models. To address this challenge, this paper introduces an innovative carbon emissions prediction model that integrates Fibonacci attenuation particle swarm optimization (FAPSO) with the gated recurrent unit (GRU). The FAPSO algorithm is used to optimize the hyperparameters of the GRU, thereby alleviating the decline in prediction accuracy that conventional recurrent neural networks often face when dealing with limited training data. To evaluate the effectiveness of the FAPSO-GRU model, we tested it using carbon emission data from Hainan Province. Compared to the conventional GRU model, the FAPSO-GRU model achieved a significant reduction in the mean absolute error (42.27%), root mean square error (42.38%), and mean absolute percentage error (43.06%). Furthermore, we validated the FAPSO-GRU model with real data from Beijing, Guangdong, Hubei, Hunan, and Shanghai. The experimental results convincingly demonstrate that the proposed model provides a highly accurate solution for carbon emission prediction tasks, effectively addressing the limitations posed by limited training data.
Full article
(This article belongs to the Section Energy Systems)
Open AccessReview
Digital Twin Implementation in Additive Manufacturing: A Comprehensive Review
by
Sabrine Ben Amor, Nessrine Elloumi, Ameni Eltaief, Borhen Louhichi, Nashmi H. Alrasheedi and Abdennour Seibi
Processes 2024, 12(6), 1062; https://doi.org/10.3390/pr12061062 - 22 May 2024
Abstract
The additive manufacturing (AM) field is rapidly expanding, attracting significant scientific attention. This family of processes will be widely used in the evolution of Industry 4.0, particularly in the production of customized components. However, as the complexity and variability of additive manufacturing processes
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The additive manufacturing (AM) field is rapidly expanding, attracting significant scientific attention. This family of processes will be widely used in the evolution of Industry 4.0, particularly in the production of customized components. However, as the complexity and variability of additive manufacturing processes increase, there is an increasing need for advanced techniques to ensure quality control, optimize performance, and reduce production costs. Multiple tests are required to optimize processing variables for specific equipment and processes, to achieve optimum processing conditions. The application of digital twins (DTs) has significantly enhanced the field of additive manufacturing. A digital twin, abbreviated as DT, refers to a computer-generated model that accurately depicts a real-world object, system, or process. A DT comprises the complete additive manufacturing process, from the initial conception phase to the final manufacturing phase. It enables the manufacturing process to be continuously monitored, studied, and optimized in real time. DT has emerged as an important tool in the additive manufacturing industry. They allow manufacturers to enhance the process, improve product quality, decrease costs, and accelerate innovation. However, the development of DT in AM is an iterative and continuous process. It requires collaboration between domain experts, data scientists, engineers, and manufacturing teams to guarantee an accurate representation of the process by the digital twin. This paper aims to provide a comprehensive analysis of the current state of DT for additive manufacturing, examining their applications, benefits, challenges, and future directions.
Full article
(This article belongs to the Section Manufacturing Processes and Systems)
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Open AccessArticle
Study on Structure Dynamic Characteristics for Internal Components of Kaplan Turbine Runner under Different Contact Modes
by
Chengming Liu, Haiqiang Luo, Guiyu Wang, Xiaobin Chen, Lingjiu Zhou and Zhengwei Wang
Processes 2024, 12(6), 1061; https://doi.org/10.3390/pr12061061 - 22 May 2024
Abstract
The stress and fatigue of the runner during the operation of the large Kaplan turbine are one of the key issues in the operation of turbines. Due to the complexity of the working load and the geometric configuration of the Kaplan turbine runner,
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The stress and fatigue of the runner during the operation of the large Kaplan turbine are one of the key issues in the operation of turbines. Due to the complexity of the working load and the geometric configuration of the Kaplan turbine runner, the different contact modes between the internal components of the runner will have an impact on the stress and fatigue results. Therefore, the unsteady CFD calculation of the full channel is conducted in this article to analyze the hydraulic characteristics of the turbine blades in the unsteady flow field, such as pressure and torque. The pressure load is loaded onto the runner using a fluid–structure interaction (FSI) theory, and the stress characteristics of the blade, blade lever, and runner body are compared under three contact modes. Based on the dynamic stress spectrum of the blade lever calculated under three contact conditions, the life of the blade lever is predicted using the rain flow counting method and the Palmgren–Miner theory. The results indicate that the rotation of the runner has a significant impact on the hydraulic and structural characteristics of the Kaplan turbine. The non-uniform and asymmetric stress and torque conditions gradually cause fatigue in the components of the runner. The average and amplitude of dynamic stress on the blade, blade lever, and runner body under frictional and frictionless contact are greater than those of fixed contact. The life of the blade lever calculated under fixed contact is much greater than that under frictional and frictionless contact; therefore, the contact conditions have a significant impact on the structural characteristics of the runner.
Full article
(This article belongs to the Section Manufacturing Processes and Systems)
Open AccessArticle
The Influence of Exogenous Particles on Saliva Rheology
by
Agata Penconek, Rafał Przekop, Urszula Michalczuk and Arkadiusz Moskal
Processes 2024, 12(6), 1060; https://doi.org/10.3390/pr12061060 - 22 May 2024
Abstract
This study aimed to investigate the effect of exogenous nanoparticles on the rheological properties of artificial saliva. There are four reasons for undertaking this type of research: Firstly, the number of solid particles of various origins present in the air is still high.
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This study aimed to investigate the effect of exogenous nanoparticles on the rheological properties of artificial saliva. There are four reasons for undertaking this type of research: Firstly, the number of solid particles of various origins present in the air is still high. Secondly, nanoparticles (including silver and gold nanoparticles) are increasingly used in food packaging and can migrate into food. Thirdly, saliva is the first biological fluid that comes into contact with exogenous particles. Finally, the function of saliva is also closely related to its rheological properties. Due to the remarkable properties of nano-objects, nanoparticles of various origins in the body may cause effects that have not been realised until now. Therefore, each type of nanoparticle must be tested in terms of its impact on the body/body fluid. We used silver and gold nanoparticles because they are used in the food industry, and diesel exhaust particles because they are standard components of air pollution. The effect of various nanoparticles (e.g., their size and shape) on the rheology of saliva at two temperatures was investigated. The constants of the power law constitutive equation were also estimated. Studies showing the impact of nanoparticles on the rheology of body fluids are rare because it is one of the less obvious ways of their affecting the human body. However, the results show that nanoparticles are not neutral to the biological fluid, which may translate into a change in its properties and thus disturb its functions.
Full article
(This article belongs to the Special Issue Features, Reviews and Perspectives for the 10th Anniversary of Processes)
Open AccessArticle
Techno-Economic and Environmental Impact Analysis of a 50 MW Solar-Powered Rankine Cycle System
by
Abdulrazzak Akroot and Abdullah Sultan Al Shammre
Processes 2024, 12(6), 1059; https://doi.org/10.3390/pr12061059 - 22 May 2024
Abstract
The interest in combined heat and solar power (CHP) systems has increased due to the growing demand for sustainable energy with low carbon emissions. An effective technical solution to address this requirement is using a parabolic trough solar collector (PTC) in conjunction with
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The interest in combined heat and solar power (CHP) systems has increased due to the growing demand for sustainable energy with low carbon emissions. An effective technical solution to address this requirement is using a parabolic trough solar collector (PTC) in conjunction with a Rankine cycle (RC) heat engine. The solar-powered Rankine cycle (SPRC) system is a renewable energy technology that can be relied upon for its high efficiency and produces clean energy output. This study describes developing a SPRC system specifically for electricity generation in Aden, Yemen. The system comprises parabolic trough collectors, a thermal storage tank, and a Rankine cycle. A 4E analysis of this system was theoretically investigated, and the effects of various design conditions, namely the boiler’s pinch point temperature and steam extraction from the high-pressure turbine, steam extraction from the intermediate-pressure turbine, and condenser temperature, were studied. Numerical simulations showed that the system produces a 50 MW net. The system’s exergetic and energy efficiencies are 30.7% and 32.4%. The planned system costs 2509 USD/h, the exergoeconomic factor is 79.43%, and the system’s energy cost is 50.19 USD/MWh. The system has a 22.47 kg/MWh environmental carbon footprint. It is also observed that the performance of the cycle is greatly influenced by climatic circumstances. Raising the boiler’s pinch point temperature decreases the system’s performance and raises the environmental impact.
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(This article belongs to the Special Issue Energy Storage Systems and Thermal Management)
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Open AccessArticle
The Distribution Pattern of Calcium Carbonate Crystallization in Tunnel Drainage Pipes
by
Wuzhao Zhou, Shaojie Guan, Shiyang Liu, Yehao Wang, Yugang Cheng, Tianwei Zhao, Liang Cheng and Tianzhuo Qin
Processes 2024, 12(6), 1058; https://doi.org/10.3390/pr12061058 - 22 May 2024
Abstract
Severe blockages of tunnel drainage systems greatly affect the lining structure of the tunnels, thus jeopardizing their stability and safety. In order to study the blockages of tunnel drainage pipes, the flow rate of a calcium carbonate crystal tunnel was measured in the
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Severe blockages of tunnel drainage systems greatly affect the lining structure of the tunnels, thus jeopardizing their stability and safety. In order to study the blockages of tunnel drainage pipes, the flow rate of a calcium carbonate crystal tunnel was measured in the mountainous area of Southwest China. According to the actual flow velocity results, numerical simulation was combined with finite element software (ANSYS Fluent). This analyzed the calcium carbonate crystallization near the interface of the tunnel drainage pipe. The results are as follows: (1) for both the Y-shaped three-way pipe and the T-shaped pipe, the values of maximum water velocity are similar but occur at different locations. At the interface of the transverse drainage pipes, flow velocity is the highest; (2) at the three-way joint segment, the water that flows in the longitudinal drainage blind tube is influenced by the water coming from the annular drainage blind tube. At the interface of the transverse drainage pipe, water flows at a lower speed in the Y-shaped three-way pipe than in the T-shaped pipe—the difference is about 3.75 times; (3) the smoothness of calcium carbonate deposition is correlated with water velocity and the content of calcium carbonate. The calcium carbonate crystal will occupy a larger space at locations with a higher calcium carbonate content and a lower flow velocity; (4) the drainage capacity of tunnel drainage pipes declines most when the volume fraction of calcium carbonate reaches 80%. Compared with the situation when calcium carbonate does not exist, the drainage capacity decreases by 84.78% for T-shaped pipe and by 77.64% for Y-shaped three-way pipe when the volume fraction of calcium carbonate is 80%.
Full article
(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)
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Open AccessArticle
Multi-Porous Medium Characterization Reveals Tight Oil Potential in the Shell Limestone Reservoir of the Sichuan Basin
by
Guangzhao Zhou, Zanquan Guo, Dongjun Wu, Saihong Xue, Minjie Lin, Wantong Wang, Zihan Zhen and Qingsheng Jin
Processes 2024, 12(6), 1057; https://doi.org/10.3390/pr12061057 - 22 May 2024
Abstract
With the continuous deepening of oil and gas exploration and development, unconventional oil and gas resources, represented by tight oil, have become research hotspots. However, few studies have investigated tight oil potential in any systematic way in the shell limestone reservoir of the
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With the continuous deepening of oil and gas exploration and development, unconventional oil and gas resources, represented by tight oil, have become research hotspots. However, few studies have investigated tight oil potential in any systematic way in the shell limestone reservoir of the Sichuan Basin. Herein, we used thin section analysis, X-ray diffraction (XRD), high-pressure mercury intrusion, low-pressure N2 and CO2 adsorption experiments, low-field nuclear magnetic resonance (NMR), focused ion beam–scanning electron microscopy (FIB-SEM), and nano-CT to characterize multi-porous media. The reservoir space controlled by nonfabric, shell, and matrix constitutes all the reservoir space for tight oil. The interconnected porosity was mainly distributed in the range of 1% to 5% (avg. 2.12%). The effective interconnected porosity mainly ranged from 0.5% to 2.0% (avg. 1.59%). The porosity of large fractures was 0.1% to 0.5% (avg. 0.21%). The porosity of isolated pores and bound oil–water pores was 0.2% to 0.8% (avg. 0.44%). The dissolved pores adjacent to fractures, the microfractures controlled by the shell, the microfractures controlled by the matrix, the isolated pores, and the intracrystalline pores constitute five independent pore-throat systems. The development of pores and fractures in shell limestone reservoirs are coupled on the centimeter–millimeter–micron–nanometer scale. Various reservoir-permeability models show continuous distribution characteristics. These findings make an important contribution to the exploration and exploitation of tight oil in shell limestone.
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(This article belongs to the Section Energy Systems)
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A Model and Data Hybrid-Driven Method for Operational Reliability Evaluation of Power Systems Considering Endogenous Uncertainty
by
Lingzi Zhu, Qihui Chen, Mingshun Liu, Lingxiao Zhang and Dongxu Chang
Processes 2024, 12(6), 1056; https://doi.org/10.3390/pr12061056 - 22 May 2024
Abstract
Renewable energy sources are increasingly integrated into power systems, leading to significant variability in operations. This necessitates robust methods for assessing operational reliability. We propose a novel model–data hybrid approach that incorporates endogenous uncertainty into the reliability evaluation process. First, unlike traditional methods
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Renewable energy sources are increasingly integrated into power systems, leading to significant variability in operations. This necessitates robust methods for assessing operational reliability. We propose a novel model–data hybrid approach that incorporates endogenous uncertainty into the reliability evaluation process. First, unlike traditional methods that treat uncertainties as external factors, this approach recognizes that operational decisions can significantly influence how uncertainties are resolved and impact reliability metrics. The proposed method integrates device reliability indices with operational decision variables. This allows us to evaluate the impact of endogenous uncertainty on operational reliability through a reliability-constrained stochastic unit commitment model. Additionally, a model–data hybrid algorithm is introduced for efficient solution of the formulated optimization problem. Case studies demonstrate the effectiveness of the proposed method. Results also show that endogenous uncertainty may cause a 10% error in power system reliability indices.
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(This article belongs to the Special Issue AC and DC Power Grids System Technologies: Analysis, Control and Practical Applications)
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Novel Hydrazide Hydrazone Derivatives as Antimicrobial Agents: Design, Synthesis, and Molecular Dynamics
by
Fatimah Agili
Processes 2024, 12(6), 1055; https://doi.org/10.3390/pr12061055 - 22 May 2024
Abstract
Ester 2 was produced by reacting thiourea derivative 1 with ethyl 2-chloro-3-oxobutanoate in MeOH containing piperidine. Hydrazide 3 was produced by reacting the latter ester with hydrazine hydrate in EtOH at reflux. By reacting hydrazide 3 with aromatic/heterocyclic aldehydes, twelve derivatives of hydrazide
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Ester 2 was produced by reacting thiourea derivative 1 with ethyl 2-chloro-3-oxobutanoate in MeOH containing piperidine. Hydrazide 3 was produced by reacting the latter ester with hydrazine hydrate in EtOH at reflux. By reacting hydrazide 3 with aromatic/heterocyclic aldehydes, twelve derivatives of hydrazide hydrazone 5a–l were produced. Spectral measurements and elemental analysis verified the molecular structure. Compounds 2, 5a, 5c, 5d, and 5f had strong effects on all the pathogenic bacterial strains according to an evaluation of the antimicrobial qualities of the synthetic compounds. With inhibitory zone diameters ranging from 16 to 20.4 mm, hydrazide hydrazone 5f exhibited the strongest activity. Additionally, the minimum inhibitory concentration (MIC) was assessed. The best outcomes were found with hydrazones 5c and 5f. For B. subtilis, the MIC of 5c was 2.5 mg/mL. For E. coli and K. pneumoniae, the MIC of 5f was 2.5 mg/mL. The molecular mechanics-generalized born surface area (MM/GBSA) was utilized to compute binding free energies via a molecular dynamics simulation analysis of the most active compounds, 5f and 5c. Moreover, computational analyses demonstrated that 5f had a substantial affinity for the active site of DNA gyrase B, suggesting that this compound could be a strong platform for new structure-based design efforts.
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(This article belongs to the Section Chemical Processes and Systems)
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Defect Identification of 316L Stainless Steel in Selective Laser Melting Process Based on Deep Learning
by
Wei Yang, Xinji Gan and Jinqian He
Processes 2024, 12(6), 1054; https://doi.org/10.3390/pr12061054 - 22 May 2024
Abstract
In additive manufacturing, such as Selective Laser Melting (SLM), identifying fabrication defects poses a significant challenge. Existing identification algorithms often struggle to meet the precision requirements for defect detection. To accurately identify small-scale defects in SLM, this paper proposes a deep learning model
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In additive manufacturing, such as Selective Laser Melting (SLM), identifying fabrication defects poses a significant challenge. Existing identification algorithms often struggle to meet the precision requirements for defect detection. To accurately identify small-scale defects in SLM, this paper proposes a deep learning model based on the original YOLOv5 network architecture for enhanced defect identification. Specifically, we integrate a small target identification layer into the network to improve the recognition of minute anomalies like keyholes. Additionally, a similarity attention module (SimAM) is introduced to enhance the model’s sensitivity to channel and spatial features, facilitating the identification of dense target regions. Furthermore, the SPD-Conv module is employed to reduce information loss within the network and enhance the model’s identification rate. During the testing phase, a set of sample photos is randomly selected to evaluate the efficacy of the proposed model, utilizing training and test sets derived from a pre-existing defect database. The model’s performance in multi-category recognition is measured using the average accuracy metric. Test results demonstrate that the improved YOLOv5 model achieves a mean average precision (mAP) of 89.8%, surpassing the mAP of the original YOLOv5 network by 1.7% and outperforming other identification networks in terms of accuracy. Notably, the improved YOLOv5 model exhibits superior capability in identifying small-sized defects.
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(This article belongs to the Special Issue Additive Manufacturing of Materials: Process and Applications)
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Three-Dimensional VOF-DEM Simulation Study of Particle Fluidization Induced by Bubbling Flow
by
Liming Liu, Mengqin Zhan, Rongtao Wang and Yefei Liu
Processes 2024, 12(6), 1053; https://doi.org/10.3390/pr12061053 - 21 May 2024
Abstract
The bubbling flow plays a key role in gas–liquid–solid fluidized beds. To understand the intrinsic fluidization behaviors at the discrete bubble and particle scale, coupled simulations with the volume of fluid model and the discrete element method are performed to investigate the effects
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The bubbling flow plays a key role in gas–liquid–solid fluidized beds. To understand the intrinsic fluidization behaviors at the discrete bubble and particle scale, coupled simulations with the volume of fluid model and the discrete element method are performed to investigate the effects of the gas inlet velocity, particle properties and two-orifice bubbling flow on particle fluidization. Three-dimensional simulations are carried out to accurately capture the dynamic changes in the bubble shape and trajectory. A bubbling flow with a closely packed bed is simulated to study the onset of particle fluidization. The obvious phenomena of particle fluidization are presented by both the experiment and simulation. Although an increasing gas inlet velocity promotes particle fluidization, the good fluidization of particles cannot be achieved solely by increasing the gas inlet velocity. When the channel is packed with more particles, the bubbles take a longer time to pass through the higher particle bed, and the bubbles grow larger in the bed. The increase in particle density also extends the time needed for the bubbles to escape from the bed, and it is more difficult to fluidize the particles with a larger density. Even if more particles are added into the channel, the percentage of suspended particles is not significantly changed. The percentage of suspended particles is not increased with a decrease in the particle diameter. The particle suspension is not significantly improved by the bubbling flow with two orifices, while the particle velocity is increased due to the more frequent bubble–particle collisions. The findings from this study will be beneficial in guiding the enhancement of particle fluidization in multiphase reactors.
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(This article belongs to the Special Issue Dynamic Modelling and Simulation of Granular Materials in Multiphase Systems)
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