Processes

18 pages, 9004 KiB

Open AccessArticle

Development and Validation of an Artificial Neural-Network-Based Optical Density Soft Sensor for a High-Throughput Fermentation System

by Matthias Medl, Vignesh Rajamanickam, Gerald Striedner and Joseph Newton

Processes 2023, 11(1), 297; https://doi.org/10.3390/pr11010297 - 16 Jan 2023

Cited by 8 | Viewed by 2579

Abstract

Optical density (OD) is a critical process parameter during fermentation, this being directly related to cell density, which provides valuable information regarding the state of the process. However, to measure OD, sampling of the fermentation broth is required. This is particularly challenging for [...] Read more.

Optical density (OD) is a critical process parameter during fermentation, this being directly related to cell density, which provides valuable information regarding the state of the process. However, to measure OD, sampling of the fermentation broth is required. This is particularly challenging for high-throughput-microbioreactor (HT-MBR) systems, which require robotic liquid-handling (LiHa) systems for process control tasks, such as pH regulation or carbon feed additions. Bioreactor volume is limited and automated at-line sampling occupies the resources of LiHa systems; this affects their ability to carry out the aforementioned pipetting operations. Minimizing the number of physical OD measurements is therefore of significant interest. However, fewer measurements also result in less process information. This resource conflict has previously represented a challenge. We present an artificial neural-network-based soft sensor developed for the real-time estimation of the OD in an MBR system. This sensor was able to estimate the OD to a high degree of accuracy (>95%), even without informative process variables stemming from, e.g., off-gas analysis only available at larger scales. Furthermore, we investigated and demonstrated scaling of the soft sensor’s generalization capabilities with the data from different antibody fragments expressing Escherichia coli strains. This study contributes to accelerated biopharmaceutical process development. Full article

(This article belongs to the Section Process Control and Monitoring)

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14 pages, 5264 KiB

Open AccessArticle

The Impact of Coronavirus Disease of 2019 (COVID-19) Lockdown Restrictions on the Criteria Pollutants

by Puneet Verma, Sohil Sisodiya, Sachin Kumar Banait, Subhankar Chowdhury, Gaurav Dwivedi and Ali Zare

Processes 2023, 11(1), 296; https://doi.org/10.3390/pr11010296 - 16 Jan 2023

Cited by 3 | Viewed by 1393

Abstract

Air pollution is accountable for various long-term and short-term respiratory diseases and even deaths. Air pollution is normally associated with a decreasing life expectancy. Governments have been implementing strategies to improve air quality. However, natural events have always played an important role in [...] Read more.

Air pollution is accountable for various long-term and short-term respiratory diseases and even deaths. Air pollution is normally associated with a decreasing life expectancy. Governments have been implementing strategies to improve air quality. However, natural events have always played an important role in the concentration of air pollutants. In Australia, the lockdown period followed the Black Summer of 2019–2020 and coincided with the season of prescribed burns. This paper investigates the changes in the concentration of criteria pollutants such as particulate matter, nitrogen dioxide, ozone, and sulphur dioxide. The air quality data for the lockdown period in 2020 was compared with the pre-lockdown period in 2020 and with corresponding periods of previous years from 2016 to 2019. The results were also compared with the post-lockdown scenario of 2020 and 2021 to understand how the concentration levels changed due to behavioural changes and a lack of background events. The results revealed that the COVID-19 restrictions had some impact on the concentration of pollutants; however, the location of monitoring stations played an important role. Full article

(This article belongs to the Special Issue Air Quality Monitoring for Smart Cities and Industrial Applications)

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17 pages, 2288 KiB

Open AccessArticle

Dynamic Optimal Decision Making of Innovative Products’ Remanufacturing Supply Chain

by Lang Liu, Zhenwei Liu, Yutao Pu and Nan Wang

Processes 2023, 11(1), 295; https://doi.org/10.3390/pr11010295 - 16 Jan 2023

Cited by 2 | Viewed by 1310

Abstract

In order to realize the recyclability of innovative product resources, we explored the optimal dynamic path of each decision variable in the remanufacturing supply chain and analyzed the impact of each decision variable on supply chain performance. Based on the Bass innovation diffusion [...] Read more.

In order to realize the recyclability of innovative product resources, we explored the optimal dynamic path of each decision variable in the remanufacturing supply chain and analyzed the impact of each decision variable on supply chain performance. Based on the Bass innovation diffusion model, we established a remanufacturing supply chain model in which a single manufacturer leads and a single retailer follows, and the retailer is responsible for recycling. The optimal wholesale price, retail price, and recovery effort path were obtained through optimal control theory. We also discussed the influence of different innovation coefficients and imitation coefficients on the overall long-term profit of each member in the supply chain, and at the same time, found the optimal market share of the product. The research results show that the larger the market innovation coefficient and the imitation coefficient are, the larger the overall long-term profit of the manufacturer and the greater the market share of the product, while the overall long-term profit of the retailer and the entire supply chain will increase first and then decrease; when the innovation coefficient and imitation coefficient are above a certain level, retailers will not enter the market. In a market with a small innovation coefficient and a large imitation coefficient, the overall long-term profits of retailers and supply chains will be higher. This study provides a theoretical basis for the decision making of the remanufacturing supply chain of innovative products in a dynamic environment, and also provides guidance for the practice of nodal enterprises in the supply chain. Full article

(This article belongs to the Special Issue Sustainable Supply Chains in Industrial Engineering and Management)

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13 pages, 6189 KiB

Open AccessArticle

A New Model of Temperature Field Accounting for Acid–Rock Reaction in Acid Fracturing in Shunbei Oilfield

by Jianye Mou, Jiayuan He, Haiqian Zheng, Rusheng Zhang, Lufeng Zhang and Budong Gao

Processes 2023, 11(1), 294; https://doi.org/10.3390/pr11010294 - 16 Jan 2023

Cited by 1 | Viewed by 1379

Abstract

The Shunbei oil formation is a deep, high-temperature carbonate reservoir. Acid fracturing is an effective technology to stimulate this formation. For acid fracturing, the temperature field is fundamental information for the acid system selection, acid–rock reaction, live acid penetration distance prediction, acid fracturing [...] Read more.

The Shunbei oil formation is a deep, high-temperature carbonate reservoir. Acid fracturing is an effective technology to stimulate this formation. For acid fracturing, the temperature field is fundamental information for the acid system selection, acid–rock reaction, live acid penetration distance prediction, acid fracturing design, etc. Therefore, in this paper, we conduct a numerical study on the temperature field in acid fracturing to account for the acid–rock reaction in the Shunbei formation. Firstly, a new mathematical model of the fracture temperature field during acid fracturing is established based on the laws of mass and energy conservation and acid–rock reaction kinetics. The fracture model is based on a PKN model, which accounts for a few factors, such as the acid–rock reaction heat, acid–rock reaction rate dependence on the temperature, and the fracture width change with acid erosion. Then, the numerical mode is developed. Next, an extensive numerical study and a parameter analysis are conducted based on the model with the field data from the Shunbei formation. The study shows that the acid–rock reaction in acid fracturing has obvious effects on the temperature field, resulting in a 10~20 °C increase in the Shunbei formation. The acid–rock reaction dependence on temperature is a factor to be accounted for. The rock dissolution increases first and then decreases from the inlet to the tip of the fracture, unlike the monotonous decrease without temperature dependence. The temperature gradient is high near the inlet and then decreases gradually. Beyond half of the fracture, the temperature is close to the formation temperature. The temperature drops fast in the initial injection stage and tends to stabilize at about 50 min. Full article

(This article belongs to the Special Issue Recent Advances in Reservoir Stimulation and EOR Technology in Unconventional Reservoirs)

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17 pages, 2365 KiB

Open AccessArticle

Prediction of Winter Wheat Harvest Based on Back Propagation Neural Network Algorithm and Multiple Remote Sensing Indices

by Hong Ji, Xun He, Wanzhang Wang and Hongmei Zhang

Processes 2023, 11(1), 293; https://doi.org/10.3390/pr11010293 - 16 Jan 2023

Cited by 3 | Viewed by 1634

Abstract

Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation [...] Read more.

Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation points, and the main producing areas were from south to north. Based on Landsat 8 satellite remote sensing images, the changes in NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and NDWI (Normalized Difference Water Index) were analyzed at different growth stages of winter wheat in 2020. Multiple regression analysis and Back Propagation (BP) neural network machine learning methods were used to establish prediction models for the harvest time of winter wheat at different growth stages. The results showed that the prediction model based on a BP neural network had high accuracy. The RMSE, MAE and MAPE of the training set and the test set were 0.531 and 0.5947, 0.3001 and 0.3104, 0.0114% and 0.0119%, respectively. The prediction model of winter wheat harvest date based on BP neural network was verified in the main winter wheat producing areas of Henan province in 2020 and 2021. The average errors were 1.67 days and 2.13 days, which were less than 3 days, meeting the needs for winter wheat production and harvest. The grain water content of winter wheat at harvest time calculated by the prediction model reached the grain water standard of the wheat combine harvester. Therefore, the prediction of the winter wheat harvest time can be realized based on multiple remote sensing indicators. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Processing and Food Industries)

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13 pages, 2533 KiB

Open AccessArticle

Optimization of the Cultivation Conditions of the Green Algae Dunaliella salina by Using Simplex Method

by Najah Al-Mhanna, Michael Pistorius and Lanah Al Sammarraie

Processes 2023, 11(1), 292; https://doi.org/10.3390/pr11010292 - 16 Jan 2023

Cited by 3 | Viewed by 2505

Abstract

The green algae Dunaliella salina offers great potential for the food industry due to its high β-carotene content. To guarantee the economic profitability of cultivation, growth conditions must be improved. Therefore, the effects of pH and salinity on the cultivation of the green [...] Read more.

The green algae Dunaliella salina offers great potential for the food industry due to its high β-carotene content. To guarantee the economic profitability of cultivation, growth conditions must be improved. Therefore, the effects of pH and salinity on the cultivation of the green alga D. salina were investigated and optimized. The simplex method was applied to find the optimum of these two parameters to maximize the biomass and the cell number of D. salina. The optimum pH was found at 7 and 8 at a salt content of 50 g/L, with a biomass content of 1.09 and 1.11 g/L, respectively. The highest biomass was found at a salinity of 50 g/L, with a final biomass of 1.11 g/L. However, by using the simplex method, an optimum product yield was found at a salinity of 64 g/L and an initial pH value of 7.2. Thus, a biomass of 1.23 mg/mL was achieved. In the single observation of both parameters, 14 experiments were conducted to obtain a satisfactory result, whereas eight runs only were required with the simplex method. This leads to the conclusion that using the simplex method is a useful way to drastically reduce the number of required experiments. Full article

(This article belongs to the Special Issue Advances in Bioprocess Technology)

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14 pages, 4402 KiB

Open AccessArticle

Experimental and Numerical Study of Electrospray Pyrolysis Process for Continuous Production of TiO₂ Particles

by Ran Wei, Jian Wang, Wangliang Li, Jichuan Wu and Weicheng Yan

Processes 2023, 11(1), 291; https://doi.org/10.3390/pr11010291 - 16 Jan 2023

Viewed by 1368

Abstract

In this study, an integrated electrospray pyrolysis process was designed to continuously produce a representative nano-catalyst TiO₂. A numerical model was also developed to simulate the flow behaviors and droplet transport inside the reactor. The electric field model and particle tracking [...] Read more.

In this study, an integrated electrospray pyrolysis process was designed to continuously produce a representative nano-catalyst TiO₂. A numerical model was also developed to simulate the flow behaviors and droplet transport inside the reactor. The electric field model and particle tracking model were coupled to describe the electrospray pyrolysis process. The effects of key parameters, including electrode configurations, applied voltage, droplet charge density, and flow type of carrying gas on the electric field distribution, particle distribution, and particle collection efficiency, were investigated to help the design and optimization of the integrated electrospray pyrolysis reactor. The results show that the electric potential and electric field strength decrease rapidly with increasing distance away from the nozzle. In addition, the results show that the droplet charge is an important parameter affecting the collection efficiency. The investigation of the key parameters shows that applying a voltage on the ring and using the “gas-bleed” introduction method are more conducive to the improvement in the collection efficiency. Full article

(This article belongs to the Section Particle Processes)

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17 pages, 4177 KiB

Open AccessArticle

Study on Purging Strategy of Polymer Electrolyte Membrane Fuel Cell under Different Operation Conditions

by Shengpeng Chen, Aina Tian and Chaoling Han

Processes 2023, 11(1), 290; https://doi.org/10.3390/pr11010290 - 16 Jan 2023

Cited by 1 | Viewed by 2151

Abstract

The commercial proton exchange membrane fuel cell (PEMFC) system needs to be equipped with the capacity to survive a harsh environment, including sub-freezing temperatures. The cold start of PEMFC brings about great technical challenges, mainly due to the ice blockage in the components, [...] Read more.

The commercial proton exchange membrane fuel cell (PEMFC) system needs to be equipped with the capacity to survive a harsh environment, including sub-freezing temperatures. The cold start of PEMFC brings about great technical challenges, mainly due to the ice blockage in the components, which seriously hinders the multi physical transmission process. A multiscale, two-dimensional model was established to explore the gas purging in PEMFC under different electrochemical reaction intensities. The results indicate that the optimal case is obtained by B_3-1 with a power density of 0.796 W cm⁻², and the power density increases first and then decreases, followed by stoichiometric flow ratio (ξ) changes. It is worth noting that the water mole fraction in the PEM is closely related to the water concentration gradient. However, the differences in the initial water distribution in porous media have little bearing on the condensed water in the gas channel, and the liquid water in the gas diffusion layer (GDL) is preferably carried away ahead of other porous parts. The results also show that the increase in the purge speed and temperature can remove the excess water on GDL and the catalytic layer in a short time. For a nitrogen-based purge, the operating condition in case B_3-1 is shown as the best strategy based on the output performance and economic analysis during the shutdown and purge process. Full article

(This article belongs to the Special Issue Progresses in Electrochemical Energy Conversion and Storage—Materials, Structures and Simulation)

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20 pages, 2186 KiB

Open AccessReview

Valorization of Cereal Byproducts with Supercritical Technology: The Case of Corn

by Ádina L. Santana and Maria Angela A. Meireles

Processes 2023, 11(1), 289; https://doi.org/10.3390/pr11010289 - 16 Jan 2023

Cited by 4 | Viewed by 2737

Abstract

Ethanol and starch are the main products generated after the processing of corn via dry grinding and wet milling, respectively. Milling generates byproducts including stover, condensed distillers’ solubles, gluten meal, and the dried distillers’ grains with solubles (DDGS), which are sources of valuable [...] Read more.

Ethanol and starch are the main products generated after the processing of corn via dry grinding and wet milling, respectively. Milling generates byproducts including stover, condensed distillers’ solubles, gluten meal, and the dried distillers’ grains with solubles (DDGS), which are sources of valuable compounds for industry including lignin, oil, protein, carotenoids, and phenolic compounds. This manuscript reviews the current research scenario on the valorization of corn milling byproducts with supercritical technology, as well as the processing strategies and the challenges of reaching economic feasibility. The main products recently studied were biodiesel, biogas, microcapsules, and extracts of enriched nutrients. The pretreatment of solid byproducts for further hydrolysis to produce sugar oligomers and bioactive peptides is another recent strategy offered by supercritical technology to process corn milling byproducts. The patents invented to transform corn milling byproducts include oil fractionation, extraction of undesirable flavors, and synthesis of structured lipids and fermentable sugars. Process intensification via the integration of milling with equipment that operates with supercritical fluids was suggested to reduce processing costs and to generate novel products. Full article

(This article belongs to the Special Issue Supercritical Technology Applied to Food, Pharmaceutical and Chemical Industries)

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3 pages, 180 KiB

Open AccessEditorial

Special Issue “Recent Advances in Thermal Food Processing Technologies”

by Ching Lik Hii, Choon Hui Tan and Meng Wai Woo

Processes 2023, 11(1), 288; https://doi.org/10.3390/pr11010288 - 16 Jan 2023

Cited by 1 | Viewed by 1682

Abstract

Historically, the application of thermal heating in food processing could be dated back to the invention by Nicholas Appert (1749–1841) which is nowadays known as canning and is able to preserve food products stored in glass bottles for an extended period [...] Full article

(This article belongs to the Section Food Process Engineering)

11 pages, 1243 KiB

Open AccessArticle

Use of Roselle Calyx Wastes for the Enrichment of Biscuits: An Approach to Improve Their Functionality

by Rocio Guadalupe Hernández-Nava, José Daniel Anaya-Tacuba, María de la Luz Sánchez-Mundo, Raquel García-Barrientos, Alejandra Flores-Castro, Carmen del Pilar Suárez-Rodríguez and Vicente Espinosa-Solis

Processes 2023, 11(1), 287; https://doi.org/10.3390/pr11010287 - 16 Jan 2023

Cited by 2 | Viewed by 1803

Abstract

The objective of the present study was to evaluate the use of powder made out of Roselle Calyx Wastes (RCP) in developing a biscuit formulation with acceptable sensory value. Roselle calyxes were infused in water in a 1:10 ratio. The residual infused calyxes [...] Read more.

The objective of the present study was to evaluate the use of powder made out of Roselle Calyx Wastes (RCP) in developing a biscuit formulation with acceptable sensory value. Roselle calyxes were infused in water in a 1:10 ratio. The residual infused calyxes were dried at 50 °C for 16 h, grounded, sieved through a 50 mesh, and stored in plastic bags until used. The biscuit formulations were enriched with RCP at 0% (BC), 5% (BRCP5), 10% (BRCP10), and 15% (BRCP15). The amount of RCP added to the biscuit formulation did not change the protein content. However, the addition of RCP significantly affected the biscuit’s color; the lightness parameter (L*) decreased as the RP content increased from 69.66 to 49.04. The sensory evaluation showed that the control biscuit and the biscuit enriched with 5% of RP were the best accepted. As for the antiradical activity, the formulation with the highest activity was presented by the BRCP15 (587.43 µmol Trolox/100 g dwb). On the other hand, BRCP5 presented 189.96 µmol Trolox/100 g dwb. Therefore, the biscuit formulation with RCP at a 15% enrichment could be used to commercialize a functional product. Full article

(This article belongs to the Special Issue Screening of Bioactive Compounds from Food Processing Waste)

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17 pages, 5023 KiB

Open AccessArticle

Pore Structure Multifractal Characteristics of Coal Reservoirs in the Central and Eastern Qinshui Basin and Influencing Factors

by Chaochao Duan, Xuehai Fu, Ze Deng, Junqiang Kang, Baoxin Zhang, Jielin Lu, Xing Hong, Ruirui Dai and Xiaogang Li

Processes 2023, 11(1), 286; https://doi.org/10.3390/pr11010286 - 16 Jan 2023

Viewed by 1317

Abstract

The heterogeneity of the pore structure of coal reservoirs affects the desorption and diffusion characteristics of coalbed methane, and determining its distribution law is conducive to improving the theory of coalbed methane development. The central and eastern parts of the Qinshui Basin are [...] Read more.

The heterogeneity of the pore structure of coal reservoirs affects the desorption and diffusion characteristics of coalbed methane, and determining its distribution law is conducive to improving the theory of coalbed methane development. The central and eastern parts of the Qinshui Basin are rich in coalbed methane resources, but the heterogeneity characteristics of the pore structure of coal reservoirs are not clear. NMR has the advantages of being fast, non-destructive and full-scale, and multifractal can describe the self-similarity of NMR T2 curve at different scales so as to analyze the complexity of pore distribution. Based on this, 15 samples with different coal ranks were collected from the central and eastern Qinshui Basin (R_o,max between 1.54 and 2.78%), and quantitative pore characterization experiments such as low-field nuclear magnetic resonance (LF-NMR) and low-temperature liquid nitrogen adsorption (LTN₂A) were conducted. Based on multifractal theory, the heterogeneity law of pore structure was quantitatively evaluated, and its influencing factors were elucidated. The results showed that the BJH pore volume of coal samples in the study area ranged from 0.0005–0.0028 cm³/g, with an average of 0.0014 cm³/g, and the BET specific surface area was 0.07–2.52 m²/g, with an average of 0.41 m²/g. The NMR T₂ spectrum peaked at 0.1–1, 10–100 and 100–1000 ms, and the spectrum was mostly bimodal or trimodal, indicating that pores of different pore sizes were developed. There were great differences in the pore structure of different coal ranks; high-rank coal was dominated by micropores, and the proportion of mesopores and macropores of medium-rank coal was higher. The pore structure of coal samples showed obvious multifractal characteristics, and the fractal characteristics of the sparse region (low-value information) were more significant; they dominated the pore distribution and had a stronger influence on the distribution of pore space. Pore structure heterogeneity is closely related to the degree of coalification, and with the increase in coalification, it is closely related to coal lithotype and quality, and high mineral and inertinite contents lead to the enhancement of pore structure heterogeneity in coal reservoirs, while R_o,max, M_ad and vitrinite group contents have opposite effects. The research results provide theoretical guidance for the subsequent exploration and development of coalbed methane in the region. Full article

(This article belongs to the Special Issue Coal Chemical Structure Evolution, Coal Molecule and Methane Adsorption)

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18 pages, 10577 KiB

Open AccessArticle

Fracture Parameters Optimization and Field Application in Low-Permeability Sandstone Reservoirs under Fracturing Flooding Conditions

by Cong Lu, Li Ma, Jianchun Guo, Lin Zhao, Shiqian Xu, Bugao Chen, Yulong Zhou, Haoren Yuan and Zhibin Tang

Processes 2023, 11(1), 285; https://doi.org/10.3390/pr11010285 - 16 Jan 2023

Cited by 5 | Viewed by 1833

Abstract

To solve engineering problems in the production process after fracturing and flooding of low-permeability sandstone reservoirs, such as rapid water-cut rise and low water flooding efficiency, a method for optimizing the fracture parameters of low-permeability sandstone reservoirs under fracturing flooding conditions was proposed. [...] Read more.

To solve engineering problems in the production process after fracturing and flooding of low-permeability sandstone reservoirs, such as rapid water-cut rise and low water flooding efficiency, a method for optimizing the fracture parameters of low-permeability sandstone reservoirs under fracturing flooding conditions was proposed. A rock property test experiment was first carried out, the fracturing coefficient was defined, and an evaluation method for the brittleness index of low-permeability sandstone was established to optimize the perforation location of the fracturing reservoir. A productivity numerical model for the two-phase flow of oil–water in matrix–fracture media was established to optimize the fracture morphology under fracturing flooding conditions. The results showed that the quartz content, Young’s modulus, and peak stress mainly affected the fracturing coefficient of rock and are the key indicators for evaluating the brittleness of low-permeability sandstone reservoirs. For production wells in the direction of minimum horizontal principal stress, the swept area of water flooding should be expanded, fracture length should be optimized to 90 m, and fracture conductivity should be 20 D·cm. For fracturing production wells in the direction of maximum horizontal principal stress, the advancing speed of the water injection front should be slowed down to reduce the risk of water channeling in injection-production wells. The optimized fracture length was 80 m, and the fracture conductivity was 25 D·cm. The application of these findings can markedly improve oil production and provide a reference for optimizing the fracture parameters of low-permeability sandstone reservoirs under fracturing flooding conditions. Full article

(This article belongs to the Special Issue Advances in Enhancing Unconventional Oil/Gas Recovery)

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25 pages, 12751 KiB

Open AccessFeature PaperArticle

Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding

by Nelson Freitas, Sara Oleiro Araújo, Duarte Alemão, João Ramos, Magno Guedes, José Gonçalves, Ricardo Silva Peres, Andre Dionisio Rocha and José Barata

Processes 2023, 11(1), 284; https://doi.org/10.3390/pr11010284 - 16 Jan 2023

Viewed by 3255

Abstract

The energy consumption of production processes is increasingly becoming a concern for the industry, driven by the high cost of electricity, the growing concern for the environment and the greenhouse emissions. It is necessary to develop and improve energy efficiency systems, to reduce [...] Read more.

The energy consumption of production processes is increasingly becoming a concern for the industry, driven by the high cost of electricity, the growing concern for the environment and the greenhouse emissions. It is necessary to develop and improve energy efficiency systems, to reduce the ecological footprint and production costs. Thus, in this work, a system is developed capable of extracting and evaluating useful data regarding production metrics and outputs. With the extracted data, machine learning-based models were created to predict the expected energy consumption of an automotive spot welding, proving a clear insight into how the input values can contribute to the energy consumption of each product or machine, but also correlate the real values to the ideal ones and use this information to determine if some process is not working as intended. The method is demonstrated in real-world scenarios with robotic cells that meet Volkswagen and Ford standards. The results are promising, as models can accurately predict the expected consumption from the cells and allow managers to infer problems or optimize schedule decisions based on the energy consumption. Additionally, by the nature of the conceived architecture, there is room to expand and build additional systems upon the currently existing software. Full article

(This article belongs to the Special Issue Digitalized Industrial Production Systems and Industry 4.0, Volume II)

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13 pages, 599 KiB

Open AccessReview

Hypomagnetic Fields and Their Multilevel Effects on Living Organisms

by Miroslava Sinčák and Jana Sedlakova-Kadukova

Processes 2023, 11(1), 282; https://doi.org/10.3390/pr11010282 - 16 Jan 2023

Cited by 4 | Viewed by 2347

Abstract

The Earth’s magnetic field is one of the basic abiotic factors in all environments, and organisms had to adapt to it during evolution. On some occasions, organisms can be confronted with a significant reduction in a magnetic field, termed a “hypomagnetic field—HMF”, for [...] Read more.

The Earth’s magnetic field is one of the basic abiotic factors in all environments, and organisms had to adapt to it during evolution. On some occasions, organisms can be confronted with a significant reduction in a magnetic field, termed a “hypomagnetic field—HMF”, for example, in buildings with steel reinforcement or during interplanetary flight. However, the effects of HMFs on living organisms are still largely unclear. Experimental studies have mostly focused on the human and rodent models. Due to the small number of publications, the effects of HMFs are mostly random, although we detected some similarities. Likely, HMFs can modify cell signalling by affecting the contents of ions (e.g., calcium) or the ROS level, which participate in cell signal transduction. Additionally, HMFs have different effects on the growth or functions of organ systems in different organisms, but negative effects on embryonal development have been shown. Embryonal development is strictly regulated to avoid developmental abnormalities, which have often been observed when exposed to a HMF. Only a few studies have addressed the effects of HMFs on the survival of microorganisms. Studying the magnetoreception of microorganisms could be useful to understand the physical aspects of the magnetoreception of the HMF. Full article

(This article belongs to the Topic Energy Efficiency, Environment and Health)

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30 pages, 2377 KiB

Open AccessReview

A Systematic Review on the Synthesis of Silicon Carbide: An Alternative Approach to Valorisation of Residual Municipal Solid Waste

by Adhithiya Venkatachalapati Thulasiraman and Mahesh Ganesapillai

Processes 2023, 11(1), 283; https://doi.org/10.3390/pr11010283 - 15 Jan 2023

Cited by 5 | Viewed by 4802

Abstract

Over the past several decades, industrialised and developing nations have attempted to enhance sustainability. Demands for energy and the acceleration in environmental deterioration are the two primary obstacles to progress. The daily generation of municipal solid waste has been a significant factor in [...] Read more.

Over the past several decades, industrialised and developing nations have attempted to enhance sustainability. Demands for energy and the acceleration in environmental deterioration are the two primary obstacles to progress. The daily generation of municipal solid waste has been a significant factor in the deterioration of the ecology. To address this issue, a considerable amount of municipal solid waste may be used to synthesise SiC nanomaterials from organic and inorganic fractions and use them as carbon and silica sources. Nanomaterials have progressively received widespread prominence as the development of particulate materials accelerates at an incredible rate. One such material is silicon carbide (SiC), which has garnered considerable interest due to its remarkable performance and wide variety of applications. This review article discusses the SiC polytypes, including cubic, hexagonal, and rhombohedral SiC. The characteristics of silicon carbide, such as its biomimetic, surface, and thermal properties, are also discussed. In addition, the synthesis of silicon carbide was described in depth, including microwave sintering, the calcination method, the carbothermal redox reaction, and much more. The final section describes the applications of silicon carbide, including wastewater treatment, medical implants, and gas detection. Full article

(This article belongs to the Section Biological Processes and Systems)

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12 pages, 3652 KiB

Open AccessArticle

Research and Application of a Quantitative Prediction Method for Sandstone Thickness in a Zone with Dense Well Pattern Development Based on an Objective Function

by Cao Li, Yongzhuo Wang, Yongbing Zhou, Xiaodong Fan, Jianfei Zhan, Guosong Chen and Zongbao Liu

Processes 2023, 11(1), 281; https://doi.org/10.3390/pr11010281 - 15 Jan 2023

Viewed by 1039

Abstract

The seismic amplitude along a layer in a section can reveal lateral reservoir changes and is one of the important means of reservoir prediction, but it is often difficult to establish the quantitative relationship between the seismic amplitude and sandstone thickness in wells [...] Read more.

The seismic amplitude along a layer in a section can reveal lateral reservoir changes and is one of the important means of reservoir prediction, but it is often difficult to establish the quantitative relationship between the seismic amplitude and sandstone thickness in wells in blocks with denser development. The use of a lower-amplitude slice for precisely quantitatively predicting the sandstone thickness, based on the goal of obtaining data on layers in the development zone with a dense well pattern, is accurate and has a valuable advantage. The method of formation slice optimization based on an objective function is studied to improve the ability of reservoir characterization by seismic attributes. This method has been applied to the reservoir prediction of the G I₂ sedimentary unit in the ZQX block of the SRT oilfield and has achieved good results. Full article

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16 pages, 102776 KiB

Open AccessFeature PaperArticle

Study on the Hydraulic Fracturing of the Inter-Salt Shale Oil Reservoir with Multi-Interfaces

by Daihong Li, Xiaoyu Zhang and Zhixiang Chen

Processes 2023, 11(1), 280; https://doi.org/10.3390/pr11010280 - 15 Jan 2023

Viewed by 1499

Abstract

Hydraulic fracture morphology and propagation mode are difficult to predict in layers of the various lithological strata, which seriously affects exploitation efficiency. This paper studies the fundamental mechanical and microscopic properties of the two main interfaces in inter-salt shale reservoirs. On this basis, [...] Read more.

Hydraulic fracture morphology and propagation mode are difficult to predict in layers of the various lithological strata, which seriously affects exploitation efficiency. This paper studies the fundamental mechanical and microscopic properties of the two main interfaces in inter-salt shale reservoirs. On this basis, cement-salt combination samples with composite interfaces are prepared, and hydraulic fracturing tests are carried out under different fluid velocities, viscosity, and stress conditions. The result shows that the shale bedding and salt-shale interface are the main geological interfaces of the inter-salt shale reservoir. The former is filled with salt, and the average tensile strength is 0.42 MPa, c = 1.473 MPa, and φ = 19.00°. The latter is well cemented, and the interface strength is greater than that of shale bedding, with c = 2.373MPa and φ = 26.15°. There are three basic fracture modes for the samples with compound interfaces. Low-viscosity fracturing fluid and high-viscosity fracturing fluid tend to open the internal bedding interface and produce a single longitudinal crack, respectively, so properly selecting the viscosity and displacement is necessary. Excessive geostress differences will aggravate the strain incompatibility of the interface between different rock properties, which makes the interfaces open easily. The pump pressure curves’ morphological characters are different with different failure modes. Full article

(This article belongs to the Special Issue Advances in Numerical Modeling for Deep Water Geo-Environment)

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13 pages, 4058 KiB

Open AccessFeature PaperArticle

Data Management of Microscale Reaction Calorimeter Using a Modular Open-Source IoT-Platform

by Timothy Aljoscha Frede, Constantin Weber, Tobias Brockhoff, Tassilo Christ, Denis Ludwig and Norbert Kockmann

Processes 2023, 11(1), 279; https://doi.org/10.3390/pr11010279 - 15 Jan 2023

Viewed by 1857

Abstract

Unifying research data collection methods and capturing data streams in an organized and standardized manner are becoming increasingly important in laboratories as digital processes and automation progressively shape the laboratory workflows. In this context, the Internet of Things (IoT) not only offers the [...] Read more.

Unifying research data collection methods and capturing data streams in an organized and standardized manner are becoming increasingly important in laboratories as digital processes and automation progressively shape the laboratory workflows. In this context, the Internet of Things (IoT) not only offers the opportunity to minimize time-consuming and repetitive tasks by delegating them to machines, but it also supports scientists in curating data. As a contribution to the establishment of IoT tools in academic research laboratories, a microscale reaction calorimeter is exemplarily connected to a modular open-source IoT-platform. The microcalorimeter’s process data is streamed to the data platform for data repository and analysis. Advantages of the platform from academia’s point of view are presented. Finally, the application of the platform was successfully tested with the hydrolysis of acetic anhydride. The data were accessed and analyzed exclusively via the IoT-platform, which provided important advantages for the operator in terms of standardized evaluation in just a few steps. Full article

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14 pages, 7641 KiB

Open AccessArticle

Gas Pipeline Leakage Detection Method Based on IUPLCD and GS-TBSVM

by Haiou Shan and Yongqiang Zhu

Processes 2023, 11(1), 278; https://doi.org/10.3390/pr11010278 - 15 Jan 2023

Cited by 1 | Viewed by 1226

Abstract

To improve the identification accuracy of gas pipeline leakage and reduce the false alarm rate, a pipeline leakage detection method based on improved uniform-phase local characteristic-scale decomposition (IUPLCD) and grid search algorithm-optimized twin-bounded support vector machine (GS-TBSVM) was proposed. First, the signal was [...] Read more.

To improve the identification accuracy of gas pipeline leakage and reduce the false alarm rate, a pipeline leakage detection method based on improved uniform-phase local characteristic-scale decomposition (IUPLCD) and grid search algorithm-optimized twin-bounded support vector machine (GS-TBSVM) was proposed. First, the signal was decomposed into several intrinsic scale components (ISC) by the UPLCD algorithm. Then, the signal reconstruction process of UPLCD was optimized and improved according to the energy and standard deviation of the amplitude of each ISC, the ISC components dominated by the signal were selected for signal reconstruction, and the denoised signal was obtained. Finally, the TBSVM was optimized using a grid search algorithm, and a GS-TBSVM model for pipeline leakage identification was constructed. The input of the GS-TBSVM model was the data processed by the IUPLCD algorithm, and the output was the real-time working conditions of the gas pipeline. The experimental results show that IUPLCD can effectively filter the noise in the signal and GS-TBSVM can accurately judge the working conditions of the gas pipeline, with a maximum identification accuracy of 98.4%. Full article

(This article belongs to the Special Issue Process Monitoring and Fault Diagnosis)

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22 pages, 5646 KiB

Open AccessArticle

Predictive Control Method of Reaming up in the Raise Boring Process Using Kernel Based Extreme Learning Machine

by Guoye Jing, Wei Yan and Fuwen Hu

Processes 2023, 11(1), 277; https://doi.org/10.3390/pr11010277 - 14 Jan 2023

Viewed by 1685

Abstract

Raise boring is an important method to construct the underground shafts of mines and other underground infrastructures, by drilling down the pilot hole and then reaming up to the desired diameter. Seriously different from the drilling operations of the mechanical parts in mechanized [...] Read more.

Raise boring is an important method to construct the underground shafts of mines and other underground infrastructures, by drilling down the pilot hole and then reaming up to the desired diameter. Seriously different from the drilling operations of the mechanical parts in mechanized mass production, it is very difficult to obtain a good consistency in the construction environments of each raise or shaft, to be more exact, every construction process is highly customized. The underground bottom-up reaming process is impossible to be observed directly, and the rock breaking effect is very difficult to be measured in real-time, due to the rock debris freely falling under the excavated shaft. The optimal configurations of the operational parameters in the drilling and working pressures, torque, rotation speed and penetration speed, mainly depend on the accumulation of construction experience or empirical models. To this end, we presented a machine learning method, based on the extreme learning machine, to determine in real-time, the relationships between the working performance and the operational parameters, and the physical-mechanical properties of excavated geologic zones, aiming at a higher production or excavation rate, safer operation and minimum ground disturbance. This research brings out new possibilities to revolutionize the process planning paradigm of the raise boring method that traditionally depends on experience or subject matter expertise. Full article

(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)

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13 pages, 2558 KiB

Open AccessArticle

Protective Effect of Annona muricata Linn Fruit Pulp Lyophilized Powder against Paracetamol-Induced Redox Imbalance and Hepatotoxicity in Rats

by Seema Menon, Rasha A. Al-Eisa, Hamida Hamdi, Lincy Lawrence, P. S. Syamily, Vipin P. Sivaram, Jose Padikkala, Shaji E. Mathew and Arunaksharan Narayanankutty

Processes 2023, 11(1), 276; https://doi.org/10.3390/pr11010276 - 14 Jan 2023

Cited by 1 | Viewed by 2039

Abstract

In the current investigation, Annona muricata Linn. lyophilized fruit pulp powder was evaluated for its hepatoprotective activity induced by paracetamol or acetaminophen (APAP). Male Sprague Dawley rats were orally pre-treated for 15 days with A. muricata lyophilized fruit pulp powder at low (1 [...] Read more.

In the current investigation, Annona muricata Linn. lyophilized fruit pulp powder was evaluated for its hepatoprotective activity induced by paracetamol or acetaminophen (APAP). Male Sprague Dawley rats were orally pre-treated for 15 days with A. muricata lyophilized fruit pulp powder at low (1 g/kg b.wt) and high doses (2 g/kg b.wt). Silymarin (100 mg/kg) was administered as the standard drug. Hepatotoxicity was induced using APAP, in a single oral administration of 2.5 g/kg body weight dosage on the 15th day. Aspartate transaminase (AST), alanine transaminase (ALT), and alkaline phosphatase (ALP) were elevated in the APAP group but were found to be significantly reduced in the pre-treated groups in a dose-dependent manner. APAP administration brought down the serum total protein and albumin levels significantly. The activities of superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase were reduced in the APAP administration; further, the reduced glutathione pool in the tissue was also diminished significantly. However, with the administration of Annona lyophilized fruit pulp powder, the level of antioxidant parameters was near normal. A significant increase in lipid peroxidation was observed in the APAP group, while the silymarin, AML, and AMH groups exhibited resistance to lipid peroxidation (LPO), as evident from lower levels of LPO generated. Histopathological examination also revealed considerable tissue damage in the APAP alone treatment group, which was not devastating in the silymarin, AML, and AMH groups. Altogether, the study concludes that the lyophilized fruit pulp of A. muricata is protective against APAP-induced liver injury in rats by modulating the hepatic redox systems. Full article

(This article belongs to the Special Issue Bioactive Compounds from Natural Plants)

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13 pages, 1406 KiB

Open AccessArticle

Processing Smoked Pork Loin Using Ultrasound-Assisted Curing

by Andrea Carnero-Hernandez, Alma D. Alarcon-Rojo, Ivan A. Garcia-Galicia, Guadalupe Nelson Aguilar-Palma, Luis M. Carrillo-Lopez and Mariana Huerta-Jimenez

Processes 2023, 11(1), 275; https://doi.org/10.3390/pr11010275 - 14 Jan 2023

Viewed by 1381

Abstract

The objective of this study was to evaluate the impact of high intensity ultrasound (HIU)-assisted brining on the physicochemical characteristics and consumer preference of smoked pork loin (Longissimus dorsi, LD). LD cuts (5 × 8 × 2.5 cm, length × width [...] Read more.

The objective of this study was to evaluate the impact of high intensity ultrasound (HIU)-assisted brining on the physicochemical characteristics and consumer preference of smoked pork loin (Longissimus dorsi, LD). LD cuts (5 × 8 × 2.5 cm, length × width × height) were randomly distributed in a 2 × 2 design of two concentration of brine (5 or 10% NaCl) and two methods of brining (static, TC; or HIU for 30 min). After brining, the samples were smoked, cooled, vacuum packed and stored for 7 d at 4 °C. Weight, pH, percentage of NaCl, water-holding capacity (WHC), shear force and colour characteristics were evaluated in post-brining and smoked samples. Sensory analysis was performed to evaluate preference in appearance, taste, and texture characteristics. Weight and NaCl increased in samples post-brining. However, smoked pork samples were not significantly different among treatments. The smoked samples became more yellow and less red. Consumers preferred TC smoked pork based on this appearance characteristic. HIU improved NaCl concentrations in cured pork meat. Under these conditions, it is necessary to consider the posterior treatment that the ultrasonicated-cured meat will undergo, since part of the weight gain was lost during the smoking process. Full article

(This article belongs to the Section Food Process Engineering)

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16 pages, 7004 KiB

Open AccessArticle

Research on the Effect of Shale Core Mechanical Behavior on Casing Deformation

by Dongfeng Li, Zhanyou He, Rui Wang, Le Zhang, Heng Fan, Hailiang Nie and Zixiong Mo

Processes 2023, 11(1), 274; https://doi.org/10.3390/pr11010274 - 14 Jan 2023

Viewed by 1023

Abstract

As an unconventional, high-quality, efficient, and clean low-carbon energy, shale gas has become a new bright spot in the exploration and development of global oil and gas resources. However, with the increasing development of shale gas in recent years, the anisotropic load of [...] Read more.

As an unconventional, high-quality, efficient, and clean low-carbon energy, shale gas has become a new bright spot in the exploration and development of global oil and gas resources. However, with the increasing development of shale gas in recent years, the anisotropic load of the shale reservoir during the mining process has caused the casing to be deformed or damaged more and more seriously. In this paper, the mechanical behavior of shale core shear, triaxial and radial compression are studied using rock true compression tests, shear tests and nuclear magnetic resonance (NMR) technology. The process of macroscopic and microscopic changes of shale fractures during the tests were analyzed to predict the effect of the fracture-state changes and stress-state changes of different shale reservoirs on the casing deformation. The results show that after the shale core is damaged, the overall pore structure changes, resulting in the decrease or increase in shale porosity. During the process of triaxial pressurization, as the pressure continues to increase, there will be a critical pressure value from elastic deformation to plastic deformation. When the pressure value exceeds the critical pressure value, the shale reservoir will have strong stress sensitivity, which can easily cause wellbore collapse. The research results have important guiding significance for determining the casing deformation under shale reservoir load and preventing casing deformation failure. Full article

(This article belongs to the Special Issue Risk Assessment and Reliability Engineering of Process Operations)

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14 pages, 3516 KiB

Open AccessFeature PaperArticle

Rapid Heating of Mold: Effect of Uneven Filling Temperature on Part Morphology and Molecular Orientation

by Sara Liparoti, Daniele Sofia and Roberto Pantani

Processes 2023, 11(1), 273; https://doi.org/10.3390/pr11010273 - 14 Jan 2023

Cited by 2 | Viewed by 1260

Abstract

Mold temperature is the key parameter in determining the morphology of molded parts. Uneven temperature distribution could induce significant effects on part performances. In such cases, uneven temperature is induced to analyze the morphology developed in the molded specimens. The technology used for [...] Read more.

Mold temperature is the key parameter in determining the morphology of molded parts. Uneven temperature distribution could induce significant effects on part performances. In such cases, uneven temperature is induced to analyze the morphology developed in the molded specimens. The technology used for controlling mold temperature during the process is crucial to maintain the short processing time. This paper proposed a strategy for controlling mold temperature during the process, avoiding a significant increase in processing time. A thin electrical heater is designed and adapted below the cavity surface, allowing for the increase of the cavity surface temperature soon after the mold closure, and the fast decrease of the mold temperature soon after the filling. The effect of several heating powers and heating durations on the molecular orientation was analyzed and exploited considering the temperature and flow field realized during the process. Full article

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15 pages, 4658 KiB

Open AccessArticle

Characterization and Biological Studies of Synthesized Titanium Dioxide Nanoparticles from Leaf Extract of Juniperus phoenicea (L.) Growing in Taif Region, Saudi Arabia

by Luluah M. Al Masoudi, Abeer S. Alqurashi, Abeer Abu Zaid and Hamida Hamdi

Processes 2023, 11(1), 272; https://doi.org/10.3390/pr11010272 - 14 Jan 2023

Cited by 4 | Viewed by 2584

Abstract

Green synthesis of metal nanoparticles in nanosized form has acquired great interest in the area of nanomedicine as an environmentally friendly and cost-effective alternative compared to other chemical and physical methods. This study deals with the eco-friendly green synthesis of titanium dioxide nanoparticles [...] Read more.

Green synthesis of metal nanoparticles in nanosized form has acquired great interest in the area of nanomedicine as an environmentally friendly and cost-effective alternative compared to other chemical and physical methods. This study deals with the eco-friendly green synthesis of titanium dioxide nanoparticles (TiO₂ NPs) utilizing Juniperus phoenicea leaf extract and their characterization. The biosynthesis of TiO₂ NPs was completed in 3 h and confirmed by UV-Vis spectroscopy, a strong band at 205.4 nm distinctly revealed the formation of NPs. Transmissions electron microscopy (TEM) analysis showed the synthesized TiO₂ NPs are spherical in shape, with a diameter in a range of 10–30 nm. The XRD major peak at 27.1° congruent with the (110) lattice plane of tetragonal rutile TiO₂ phase. Dynamic light scattering (DLS) analysis revealed synthesized TiO₂ NPs average particle size (hydrodynamic diameter) of (74.8 ± 0.649) nm. Fourier transmission infrared (FTIR) revealed the bioactive components present in the leaf extract, which act as reducing and capping agents. The antimicrobial efficacy of synthesized TiO₂NPs against, Staphylococcus aureus, and Bacillus subtilis (Gram-positive), Escherichia coli and Klebsiella pneumoniae (Gram-negative), Yeast strain (Saccharomyces cerevisiae) and fungi (Aspergillus niger, and Penicillium digitatum) assayed by a disc diffusion method. TiO₂NPs inhibited all tested strains by mean inhibition zone (MIZ), which ranged from the lowest 15.7 ± 0.45 mm against K. pneumoniae to the highest 30.3 ± 0.25 against Aspergillus niger. The lowest minimum inhibitory concentration (MIC) and bactericidal (MBC) values were 20 μL/mL and 40 μL/mL of TiO₂NPs were observed against Asp. niger. Moreover, it showed significant inhibitory activity against human ovarian adenocarcinoma cells with IC₅₀ = 50.13 ± 1.65 µg/mL. The findings concluded that biosynthesized TiO₂ NPs using Juniperus phoenicea leaf extract can be used in medicine as curative agents according to their in vitro antibacterial, antifungal, and cytotoxic activities. Full article

(This article belongs to the Section Pharmaceutical Processes)

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22 pages, 1563 KiB

Open AccessArticle

Workers’ Opinions on Using the Internet of Things to Enhance the Performance of the Olive Oil Industry: A Machine Learning Approach

by Ahmed Alsayat and Hossein Ahmadi

Processes 2023, 11(1), 271; https://doi.org/10.3390/pr11010271 - 14 Jan 2023

Cited by 7 | Viewed by 2532

Abstract

Today’s global food supply chains are highly dispersed and complex. The adoption and effective utilization of information technology are likely to increase the efficiency of companies. Because of the broad variety of sensors that are currently accessible, the possibilities for Internet of Things [...] Read more.

Today’s global food supply chains are highly dispersed and complex. The adoption and effective utilization of information technology are likely to increase the efficiency of companies. Because of the broad variety of sensors that are currently accessible, the possibilities for Internet of Things (IoT) applications in the olive oil industry are almost limitless. Although previous studies have investigated the impact of the IoT on the performance of industries, this issue has yet to be explored in the olive oil industry. In this study we aimed to develop a new model to investigate the factors influencing supply chain improvement in olive oil companies. The model was used to evaluate the relationship between supply chain improvement and olive oil companies’ performance. Demand planning, manufacturing, transportation, customer service, warehousing, and inventory management were the main factors incorporated into the proposed model. Self-organizing map (SOM) clustering and decision trees were employed in the development of the method. The data were collected from respondents with knowledge related to integrating new technologies into the industry. The results demonstrated that IoT implementation in olive oil companies significantly improved their performance. Moreover, it was found that there was a positive relationship between supply chain improvements via IoT implementation in olive oil companies and their performance. Full article

(This article belongs to the Special Issue Machine Learning and Optimization Algorithms for Data Analysis and Other Engineering Applications)

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15 pages, 2794 KiB

Open AccessArticle

Catalytic-Level Identification of Prepared Pt/HY, Pt-Zn/HY, and Pt-Rh/HY Nanocatalysts on the Reforming Reactions of N-Heptane

by Ramzy S. Hamied, Khalid A. Sukkar, Hasan Shakir Majdi, Zainb Y. Shnain, Mohammed Shorbaz Graish and Luma H. Mahmood

Processes 2023, 11(1), 270; https://doi.org/10.3390/pr11010270 - 14 Jan 2023

Cited by 6 | Viewed by 1684

Abstract

The operation of reforming catalysts in a fixed bed reactor undergoes a high level of interaction between the operating parameters and the reaction mechanism. Understanding such an interaction reduces the catalyst deactivation rate. In the present work, three kinds of nanocatalysts (i.e., Pt/HY, [...] Read more.

The operation of reforming catalysts in a fixed bed reactor undergoes a high level of interaction between the operating parameters and the reaction mechanism. Understanding such an interaction reduces the catalyst deactivation rate. In the present work, three kinds of nanocatalysts (i.e., Pt/HY, Pt-Zn/HY, and Pt-Rh/HY) were synthesized. The catalysts’ performances were evaluated for n-heptane reactions in the fixed bed reactor. The operating conditions applied were the following: 1 bar pressure, WHSV of 4, hydrogen/n-heptane ratio of 4, and the reaction temperatures of 425, 450, 475, 500, and 525 °C. The optimal reaction temperature for all three types of nanocatalysts to produce high-quality isomers and aromatic hydrocarbons was 500 °C. Accordingly, the nanocatalyst Pt-Zn/HY provided the highest catalytic selectivity for the desired hydrocarbons. Moreover, the Pt-Zn/HY-nanocatalyst showed more resistance against catalyst deactivation in comparison with the other two types of nanocatalysts (Pt/HY and Pt-Rh/HY). This work offers more understanding for the application of nanocatalysts in the reforming process in petroleum refineries with high performance and economic feasibility. Full article

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21 pages, 7387 KiB

Open AccessEditor’s ChoiceArticle

Performance Monitoring of Wind Turbines Gearbox Utilising Artificial Neural Networks — Steps toward Successful Implementation of Predictive Maintenance Strategy

by Basheer Wasef Shaheen and István Németh

Processes 2023, 11(1), 269; https://doi.org/10.3390/pr11010269 - 13 Jan 2023

Viewed by 1909

Abstract

Manufacturing and energy sectors provide vast amounts of maintenance data and information which can be used proactively for performance monitoring and prognostic analysis which lead to improve maintenance planning and scheduling activities. This leads to reduced unplanned shutdowns, maintenance costs and any fatal [...] Read more.

Manufacturing and energy sectors provide vast amounts of maintenance data and information which can be used proactively for performance monitoring and prognostic analysis which lead to improve maintenance planning and scheduling activities. This leads to reduced unplanned shutdowns, maintenance costs and any fatal events that could affect the operations of the overall system. Performance and condition monitoring are among the most used strategies for prognostic and health management (PHM), in which different methods and techniques can be implemented to analyse maintenance and online data. Offshore wind turbines (WTs) are complex systems increasingly needing maintenance. This study proposes a performance monitoring system to monitor the performance of the WT power generation process by exploiting artificial neural networks (ANN) composed of different network designs and training algorithms, using simulated supervisory control and data acquisition (SCADA) data. The performance monitoring is based on different operating modes of the same type of wind turbine. The degradation models were developed based on the generated active power resulting from different degradation levels of the gearbox, which is a critical component of the WTs. The deviations of the wind power curves for all operating modes over time are monitored in terms of the resulting power residuals and are modelled using ANN with a unique network architecture. The monitoring process uses the recursive form of the cumulative summation (CUSUM) change detection algorithm to detect the state change point in which the gearbox efficiency is degraded by evaluating the power residuals predicted by the ANN model. To increase the monitoring effectiveness, a second ANN model was developed to predict the gearbox efficiency to monitor any failure that could happen once the efficiency degrades below a threshold. The results show a high degree of accuracy in power and efficiency prediction in addition to monitoring the abnormal state or deviations of the power generation process resulting from the degraded gearbox efficiency and their corresponding time slots. The developed monitoring method can be a valuable tool to provide maintenance experts with alarms and insights into the general state of the power generation process, which can be used for further maintenance decision-making. Full article

(This article belongs to the Special Issue Neural Computation and Applications for Sustainable Energy Systems)

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28 pages, 8542 KiB

Open AccessArticle

Calibration and Validation of Flow Parameters of Irregular Gravel Particles Based on the Multi-Response Concept

by Aibin Zhang, Zhaohui Wang, Quanjie Gao, Yiwei Fan and Hongxia Wang

Processes 2023, 11(1), 268; https://doi.org/10.3390/pr11010268 - 13 Jan 2023

Cited by 1 | Viewed by 1592

Abstract

The discrete element method (DEM) often uses the angle of repose to study the microscopic parameters of particles. This paper proposes a multi-objective optimization method combining realistic modeling of particles and image analysis to calibrate gravel parameters, after obtaining the actual static angle [...] Read more.

The discrete element method (DEM) often uses the angle of repose to study the microscopic parameters of particles. This paper proposes a multi-objective optimization method combining realistic modeling of particles and image analysis to calibrate gravel parameters, after obtaining the actual static angle of repose (α_{AoR_S}) and dynamic angle of repose (β_{AoR_D}) of the particles by physical tests. The design variables were obtained by Latin hypercube sampling (LHS), and the radial basis function (RBF) surrogate model was used to establish the relationship between the objective function and the design variables. The optimized design of the non-dominated sorting genetic algorithm II (NSGA-II) with the actual angle of repose measurements was used to optimize the design to obtain the best combination of parameters. Finally, the parameter set was validated by a hollow cylinder test, and the relative error between the validation test and the optimized simulation results was only 3.26%. The validation result indicates that the method can be reliably applied to the calibration process of the flow parameters of irregular gravel particles. The development of solid–liquid two-phase flow and the wear behavior of centrifugal pumps were investigated using the parameter set. The results show that the increase in cumulative tangential contact forces inside the volute of centrifugal pumps makes it the component most likely to develop wear behavior. The results also illustrate the significant meaning of the accurate application of the discrete element method for improving the efficient production of industrial scenarios. Full article

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