Processes

Hybrid electric vehicles (HEVs) have certain advantages over internal combustion engines in terms of energy consumption and emission performance. However, the transmission system parameters are uncertain. The low matching between the engine and the power transmission system makes it a big problem to improve the efficiency of hybrid vehicles. Therefore, the multi-objective optimization design of hybrid vehicles is studied. The transmission system parameters of hybrid vehicles are analyzed from the objective function, decision variables, and constraints. The NSGA-II algorithm with elite strategy is introduced to realize the optimal selection of parameters and formulation of energy consumption optimization strategy. The results showed that the multi-objective optimization algorithm could adjust the position of the working point of the engine and improve the efficiency by more than 10%. There was an average difference of 2.15% after the improvement in the fuel consumption of four-gear vehicles. The fuel consumption per 100 km decreases by more than 3%. The maximum climbing gradient of the whole vehicle was 33.9%. The power factor of the direct gear of the maximum power factor increases by 15% after the improvement. The multi-objective energy consumption optimization design of hybrid vehicles proposed in the study can effectively improve the economic and dynamic performance of the whole vehicle and reduce fuel consumption. It provides a reference for the optimization of the hybrid vehicle transmission system. Full article

As is widely known, the issue of freshwater scarcity affects practically all people, and all are looking for innovative and workable ways to attempt to solve this issue. In this work, a novel method of desalination is proposed. The proposed system consists of a solar collector (PTSC), evacuated pipe (EP), condenser (CU), and separation unit (SU). The working principle of the system is heating the feed saline water using the PTSC and EP and controlling the water flow rate to control the output conditions of the EP. The produced vapor is therefore separated from salty water using the SU. In addition, the generated steam is condensed into the CU to produce a freshwater distillate. Consequently, the effect of solar radiation on the affecting temperatures was tested. In addition, the effect of using different water flow rates (6, 7.5, 10, 20, 40, and 60 L/h) inside the EP on the system productivity was investigated. The primary findings of this work may be highlighted in relation to the experiments conducted. At midday, when ultraviolet irradiance reached its highest, the EP’s water flow entrance and outflow had the largest temperature differential. In addition, the lower the water flow rate inside the EP, the higher the water temperature, the higher the evaporation rate of the system, and the greater the freshwater productivity of the system. At 6 L/h, the water’s highest temperature was 92 °C. Moreover, the best performance of the system was obtained at 7.5 L/h, where the freshwater production and average daily effectiveness of the distillate process were 44.7 L/daytime and 59.6%, respectively. As well, the productivity of EP was augmented by around 11.86% when using graphite nanoparticles. Additionally, the distilled freshwater from the system operating at the flow rate of 7.5 L/h costs 0.0085 $/L. Full article

The production of heavy fuel oil from hydrocracked vacuum residue requires dilution of the residue with cutter stocks to reduce viscosity. The hydrocracked residue obtained from different vacuum residue blends originating from diverse crude oils may have divergent properties and interact with the variant cutter stocks in a dissimilar way leading to changeable values of density, sediment content, and viscosity of the obtained fuel oil. H-Oil hydrocracked vacuum residues (VTBs) obtained from different crude blends (Urals, Siberian Light (LSCO), and Basrah Heavy) were diluted with the high aromatic fluid catalytic cracking (FCC) light cycle, heavy cycle, and slurry oil, and the low aromatic fluid catalytic cracking feed hydrotreater diesel cutter stocks and their densities, sediment content, and viscosity of the mixtures were investigated. Intercriteria analysis evaluation of the data generated in this study was performed. It was found that the densities of the blends H-Oil VTB/cutter stocks deviate from the regular solution behavior because of the presence of attractive and repulsive forces between the molecules of the H-Oil VTB and the cutter stocks. Urals and Basrah Heavy crude oils were found to enhance the attractive forces, while the LSCO increases the repulsive forces between the molecules of H-Oil VTBs and those of the FCC gas oils. The viscosity of the H-Oil VTB obtained during hydrocracking of straight run vacuum residue blend was established to linearly depend on the viscosity of the H-Oil vacuum residue feed blend. The applied equations to predict viscosity of blends containing straight run and hydrocracked vacuum residues and cutter stocks proved their good prediction ability with an average relative absolute deviation (%AAD) of 8.8%. While the viscosity was found possible to predict, the sediment content of the blends H-Oil VTBs/cutter stocks was recalcitrant to forecast. Full article

Mine accidents are mostly caused by human unsafe behavior. To reduce the unsafe behavior of mine operation and reduce the accident of mine operation, the main body of unsafe behavior ‘people’ is analyzed, and 24 attribute factors are selected from five aspects of people’s emotions, motivation, ability, personality, and pressure to construct the comprehensive model of human behavior SMAPP (sentiment–motivation–ability–personality–pressure). The program tool for recording, saving, and executing the mutual and interactive influence relationship of 24 attribute factors under different state values and the simulation process framework of SMAPP was constructed by using 1071 rule statements written in Python language. The fuzzy rules are used to simulate different scenarios. The simulation results are consistent with the actual research results, which shows the reliability and scientificity of the model. In addition, additional events are added to the simulation process to make the model more realistic. Through the simulation results, the influence of employees’ emotions, motivations, abilities, personalities, pressures, and additional events on the unsafe behavior of mine operations is analyzed and predicted, and the measures to reduce the unsafe behavior of mine operations are further proposed. Full article

The wide availability of crude glycerol and its low market price make this by-product of the biodiesel industry a promising raw material for obtaining high-value-added products through catalytic conversion processes. This work studied the effect of the composition of different industrial crude glycerol samples on the catalytic hydrogenolysis to 1,2-propylene glycol. A nickel catalyst supported on a silica–carbon composite was employed with this purpose. This catalyst proved to be active, selective to 1,2-propylene glycol and stable in the glycerol hydrogenolysis reaction in the liquid phase when analytical glycerol (99% purity) was employed. In order to determine the effect of crude glycerol composition on the activity, selectivity and stability of this catalyst, industrial crude glycerol samples were characterized by identifying and quantifying the impurities present in them (methanol, NaOH, NaCl and NaCOOH). Reaction tests were carried out with aqueous solutions of analytical glycerol, adding different impurities one by one in their respective concentration range. These results allowed for calculating activity factors starting from the ratio between the rate of glycerol consumption in the presence and in the absence of impurities. Finally, catalyst performance was evaluated employing the industrial crude glycerol samples, and a kinetic model based on the power law was proposed, which fitted the experimental results taking into account the effect of glycerol impurities. The fit allowed for predicting conversion values with an average error below 8%. Full article

In the Industry 4.0 environment, an ideal smart factory should be intelligent, green, and humanized, and the logistics transportation from raw materials to final products in the factory should be completed by smart logistics. In order to address the problems of low efficiency, poor workstation service satisfaction, high distribution costs, and non-greening during the logistics distribution processes in discrete smart manufacturing workshops are required. A mathematical model of optimized multi-objective green logistics distribution paths in a smart manufacturing workshop has been constructed in this study, with low costs, high efficiency, and workstation service satisfaction taken into consideration. Then, this mathematical model was solved with an improved ant colony optimization algorithm. A “time window span” was introduced in the basic ant colony optimization algorithm to prioritize the services to workstations with a relatively high urgency in material demand, with the aim of improving workstation service satisfaction. Lastly, in order to verify the effectiveness of the model and algorithm proposed in this study, a simulation experiment has been conducted on the workstation logistics distribution system in a smart manufacturing workshop to provide convincing evidence for optimizing workstation logistics distribution paths in workshops of discrete manufacturing enterprises. Full article

Capsicum chinense Jacq., from the Yucatan peninsula, is recognized worldwide for its pungency, flavor, and secondary metabolites content. This has resulted in an increase in its production, which has led to an increase in the number of byproducts considered waste, mainly its leaves. Capsicum chinense leaves have been demonstrated to contain polyphenols with bioactive properties (antioxidant, anti-inflammatory, antiobesogenic capacity, etc.); hence, the extraction of polyphenols through the use of natural deep eutectic solvents (NADES) with a green technology, such as an ultrasonic probe, could help to revalue these leaves by maximizing the extraction efficiency and preserving their bioactive properties. The objective of this study was to optimize the composition of a eutectic solvent for obtaining an extract rich in polyphenols from the Capsicum chinense leaf using a sonic probe. The optimum conditions of the composition of NADES for obtaining the highest Antioxidant capacity (Ax, 79.71% inhibition) were a 0.8 mol glucose to 1 mol of choline chloride ratio, and 12% water. In addition, with this composition, the Total Polyphenol Content (TPC) obtained was 165.39 mg GAE/100 g dry leaf, and the individual polyphenols, such as vanillin (19.15 mg/100 g dry leaf) and ferulic acid (1.35 mg/100 g dry leaf), were optimized. The habanero pepper leaf extract obtained using a eutectic solvent and a sonic probe demonstrated a high potential for use as an ingredient in the development of nutraceuticals (i.e., functional foods). Full article

A thermoelectric generator (TEG) is studied by considering different leg structures of hollow/filled legs, using new cooling nanofluids, and analyzing the segmentation effect. TEG performance is characterized by power output, conversion efficiency, and exergy efficiency. This study shows the impact of different cooling nanofluids (TiO₂, graphene, and Al₂O₃) on the performance of the thermoelectric generator. Furthermore, in the comparative analysis of nanofluid cooling enhancement for TEG, different hollow/filled thermoelectric legs recently proposed in the literature are considered. Likewise, three segmentation types are used, 2n-2p, 1n-2p, and 2n-1p, thus will be compared with the results of the unsegmented legs. This study calculates the performance of thermoelectric leg structures through a validated numerical simulation on the ANSYS Workbench (modeling, design, and simulation). In addition, the optimal working conditions are evaluated. This study found that quenching of nanofluids can improve TEG performance by up to 17% compared to distilled water. However, the performance improvement of the TEG for each nanofluid is small between them. Furthermore, segmentation of n-type thermocouples improves efficiency and exergy, whereas segmentation of p-type thermocouples improves output power. The segmentation enhances performance by up to twice that of non-segmented leg structures; hollow structures are better performers. In the results, it is reported that the 2n-1p segmentation is the one with the best performance, reaching a maximum energy efficiency of 38%. The triangular leg structure improves performance by up to 75% compared to the rectangular and square leg structures. Likewise, using TiO₂ is the best cooling option with nanofluids since it improves performance by 17% compared to distilled water. Furthermore, the results of cooling nanofluids for TEG performance are useful for the design of thermoelectric leg structures and stimulate further research. Full article

Traditional wastewater treatment methods, such as reverse osmosis, adsorption, desalination, and others, are outweighed by the photocatalytic degradation of organic pollutants. Ferrites are prominent photocatalysts due to their tunable band gaps, surface areas, and magnetic properties, which render photodegradation economical. Ferrites and their nanocomposites have been reported as promising visible light active photocatalysts. The photocatalytic system is heavily reliant on a number of factors that influence the photodegradation of organic effluents. This review demonstrates various parameters such as substrate concentration, pH of solution, photocatalyst quantity, photocatalyst surface area, metal and non-metal ion doping, light intensity, irradiation time, quenchers, etc. affecting the photocatalytic degradation of organic effluents by ferrite nanoparticles and their nanocomposites in detail. The photodegradation efficiency of the ferrite nanoparticles alters with the change in the value of pH of the solution, which further depends upon the nature of the pollutant used. A dose of the substrate and the photocatalyst must be optimized so as to attain better photodegradation efficiency. Photocatalysts with different surface areas change the amount of active sites, which in turn affects the degradation of pollutant and render it a crucial factor. In addition, the mechanism of the action of photocatalysis is elaborated in this review. Future research perspectives for the advancement of ferrites and their nanocomposites are deliberated in order to improve their use as photocatalysts. Full article

This work aimed to optimize the extraction conditions of bioactive compounds obtained from three squash by-products (e.g., peel, endocarp, and seeds) using the response surface methodology (RSM). The selected independent variables were ethanol concentration, extraction time, and extraction temperature. Squash by-products’ bioactive molecules were extracted according to the matrix proposed by the experimental plan. Significant variability in total phenolic compound content (TPC) and antioxidant activity, depending on the extraction time, the solvent concentration, and the extraction temperature, was recorded for the tested by-products. The experimental results adequately fitted with second-order polynomial models and showed significant linear, quadratic, and interaction effects of the independent variables. Data analysis suggested that the optimal extraction conditions were 12.2% ethanol for 11.2 min at 55 °C for peels; 28.5% ethanol for 10.5 min at 37 °C for endocarp; and 20% ethanol for 10.5 min at 60 °C for seeds. The results obtained showed that the experimental and predicted values of TPC and antioxidant activities as an indicator of a successful extraction fit with each other, thus indicating the optimal extraction conditions. Under these conditions, the obtained extracts exhibited high, although variable, TPC with epicatechin and epigallocatechin as major compounds, as well significant antimicrobial potency, which reached 100% and 80% inhibition of the tested bacteria and fungi. Full article

Configuring energy storage devices can effectively improve the on-site consumption rate of new energy such as wind power and photovoltaic, and alleviate the planning and construction pressure of external power grids on grid-connected operation of new energy. Therefore, a dual layer optimization configuration method for energy storage capacity with source load collaborative participation is proposed. The external model introduces a demand-side response strategy, determines the peak, flat, and valley periods of the time-of-use electricity price-based on the distribution characteristics of load and new energy output, and further aims to maximize the revenue of the wind and solar storage system. With the peak, flat, and valley electricity price as the decision variable, an outer optimization model is established. Based on the optimized electricity price, the user’s electricity consumption in each period is adjusted, and the results are transmitted to the inner optimization model. The internal model takes the configuration power and energy storage capacity in the wind and solar storage system as decision variables, establishes a multi-objective function that comprehensively considers the on-site consumption rate of new energy and the cost of energy storage configuration, and feeds back the optimization results of the inner layer to the outer layer optimization model. Use ISSA-MOPSO algorithm to solve the optimized configuration model. Finally, the rationality of the proposed model and algorithm in terms of on-site consumption rate and economy of new energy is verified through numerical examples. Full article

In agro-industrial processes, microorganisms that are not pathogenic and that generate molecules are generally recognized as safe (GRAS). The Aspergillus niger fungus has different industrial applications, being used to produce citric acid and 166 other secondary metabolites. The objective of this research was to optimize a culture medium to induce the production of kojic acid (KA) by the Aspergillus niger M4 strain in a liquid fermentation process. Four fermentative kinetics were developed in flasks, using different levels of yeast extract in (1) 0.05 g/L, (2) 0.10 g/L, (3) 2.5 g/L, and (4) 2.5 g/L + Zinc sulfate. The culture medium conditions influenced the formation and speed of biomass and the synthesis and yield of KA. The optimum production points were from 72 h and 96 h with 0.552 g/L and 0.510 g/L of KA using 2.5 g/L of yeast extract and with a pH of 5.5. The Aspergillus niger M4 strain had the ability to produce kojic acid, which was induced by the concentration of the nitrogen source. Full article

The mechanism of high-intensity conditioning (HIC) has not been thoroughly revealed, and therefore this work investigates the effect of HIC on the surface hydrophobicity of coal with different particle sizes and the possible formation of particle–bubble clusters. The results show that different HIC conditions are required for coarse and fine particles. Coarse particles (+75 μm) require a higher turbulence intensity to increase collector dispersion, thereby increasing the adsorption of the collector. Fine particles (−75 μm) require a lower turbulence intensity to reduce the desorption of the collector. In this study, the optimum HIC conditions for coarse and fine particles are “2200 rpm + 1 min” and “1300 rpm + 1 min”, respectively. Interestingly, it seems that the adsorption capacity between fine particles and the collector is weaker than that for coarse particles. A non-enclosed HIC system produces up to 1.78 × 10⁴/g bubbles in coarse particle–bubble clusters, and the mean bubble diameter is approximately 87 μm. The cluster achieves pre-mineralization and increases the apparent particle size, which is expected to improve flotation. Full article

The Mikania genus has been known to possess numerous pharmacological activities. In the present study, we aimed to evaluate the interaction of 26 selected constituents of Mikania species with (i) cyclooxygenase 2 (COX 2), (ii) human neutrophil elastase (HNE), (iii) lipoxygenase (LOX), matrix metalloproteinase ((iv) MMP 2 and (v) MMP 9), and (vi) microsomal prostaglandin E synthase 2 (mPGES 2) inhibitors using an in silico approach. The 26 selected constituents of Mikania species, namely mikamicranolide, kaurenoic acid, stigmasterol, grandifloric acid, kaurenol, spathulenol, caryophyllene oxide, syringaldehyde, dihydrocoumarin, o-coumaric acid, taraxerol, melilotoside, patuletin, methyl-3,5-di-O-caffeoyl quinate, 3,3′,5-trihydroxy-4′,6,7-trimethoxyflavone, psoralen, curcumene, herniarin, 2,6-dimethoxy quinone, bicyclogermacrene, α-bisabolol, γ-elemene, provincialin, dehydrocostus lactone, mikanin-3-O-sulfate, and nepetin, were assessed based on the docking action with COX 2, HNE, LOX, MMP 2, MMP 9, and mPGES 2 using Discovery Studio (in the case of LOX, the Autodock method was utilized). Moreover, STITCH (Search Tool for Interacting Chemicals), physicochemical, drug-likeness, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analyses were conducted utilizing the STITCH web server, the Mol-inspiration web server, and Discovery Studio, respectively. In the present study, STITCH analysis revealed only six ligands (dihydrocoumarin, patuletin, kaurenol, psoralen, curcumene, and nepetin) that showed interactions with human proteins. Physicochemical analysis showed that seventeen ligands complied well with Lipinski’s rule. ADMET analysis showed eleven ligands to possess hepatotoxic effects. Significantly, the binding free energy estimation displayed that the ligand methyl-3, 5-di-O-caffeoyl quinate revealed the highest binding energy for all the target enzymes, excluding LOX, suggesting that this may have efficacy as a non-steroidal anti-inflammatory drug (NSAID). The current study presents a better understanding of how Mikania is used as a traditional medicinal plant. Specifically, the 26 ligands of the Mikania plant are potential inhibitor against COX 2, HNE, LOX, MMP 2, MMP 9, and mPGES 2 for treatments for acute and/or chronic inflammatory diseases. Full article

The remote sensing ecological index (RSEI) has been widely used in the rapid monitoring and evaluation of the regional ecological environment; however, the research on the main factors that cause changes in RSEI and the impact of human activities in the mining area on RSEI is not often explored. To this end, this paper selected the Landsat (TM/OLI) series of remote sensing images from 1986 to 2022; extracted the four important indicators of the normalized difference vegetation index (NDVI), the wetness component of the tasseled cap transformation (WET), normalized difference built-up and soil index (NDBSI), and land surface temperature (LST); calculated the remote sensing ecological index (RSEI) based on the principal component analysis method; monitored and evaluated the ecological environment changes in the Shendong Mining Area for a period of 36 years; and analyzed the driving forces that cause these ecological environment changes. The results show the following: (1) The ecological status of the study area has shown an overall upward trend during the 1986–2022 period. (2) From 1986 to 2022, the area of RSEI with a grade of 0.4–0.6 increased by 1142.74 km², that with a grade of 0.6–0.8 increased by 124.09 km², and that with a grade of 0.8–1.0 increased by 0.73 km². (3) In the past 36 years, the proportion of RSEI with a positive grade difference was 97.52%, and the proportion of regions with a negative grade difference was 6.20%. (4) Rainfall is the main factor that causes changes in the regional ecological environment. By analyzing the main driving factors of ecological environment change and the relationship between human activities and RSEI, reference can be provided for the formulation of environmental protection policies and environmental planning in mining areas. Full article

Machine learning assists with food process optimization techniques by developing a model to predict the optimal solution for given input data. Machine learning includes unsupervised and supervised learning, data pre-processing, feature engineering, model selection, assessment, and optimization methods. Various problems with food processing optimization could be resolved using these techniques. Machine learning is increasingly being used in the food industry to improve production efficiency, reduce waste, and create personalized customer experiences. Machine learning may be used to improve ingredient utilization and save costs, automate operations such as packing and labeling, and even forecast consumer preferences to develop personalized products. Machine learning is also being used to identify food safety hazards before they reach the consumer, such as contaminants or spoiled food. The usage of machine learning in the food sector is predicted to rise in the near future as more businesses understand the potential of this technology to enhance customer experience and boost productivity. Machine learning may be utilized to enhance nano-technological operations and fruit and vegetable preservation. Machine learning algorithms may find trends regarding various factors that impact the quality of the product being preserved by examining data from prior tests. Furthermore, machine learning may be utilized to determine optimal parameter combinations that result in maximal produce preservation. The review discusses the relevance of machine learning in ready-to-eat foods and its use as a safety tool for preservation were highlighted. The application of machine learning in agriculture, food packaging, food processing, and food safety is reviewed. The working principle and methodology, as well as the principles of machine learning, were discussed. Full article

When designing a new type of power plants operating on pulsed detonations of gaseous or liquid fuels, the concept of fast deflagration-to-detonation transition (FDDT) is used. According to the concept, a flame arising from a weak ignition source must accelerate so fast as to form an intense shock wave at a minimum distance from the ignition source so that the intensity of the shock wave is sufficient for fast shock-to-detonation transition by some additional arrangements. Hence, the FDDT concept implies the use of special means for flame acceleration and shock wave amplification. In this work, we study the FDDT using a pulsed detonation tube comprising a Shchelkin spiral and a helical tube section with ten coils as the means for flame acceleration and shock amplification (focusing), respectively. To attain the FDDT at the shortest distances for fuels of significantly different detonability, the diameter of the pulsed detonation tube is taken close to the limiting diameter of detonation propagation for air mixtures of regular hydrocarbon fuels (50 mm). Experiments are conducted with air mixtures of individual gaseous fuels (hydrogen, methane, propane, and ethylene) and binary fuel compositions (methane–hydrogen, propane–hydrogen, and ethylene–hydrogen) at normal pressure and temperature conditions. The use of a helical tube with ten coils is shown to considerably extend the fuel-lean concentration limits of detonation as compared to the straight tube and the tube with a helical section with two coils. Full article

The development and optimization of lipid nanoparticle (LNP) formulations through hydrodynamic mixing is critical for ensuring the efficient and cost-effective supply of vaccines. Continuous LNP formation through microfluidic mixing can overcome manufacturing bottlenecks and enable the production of nucleic acid vaccines and therapeutics. Predictive process models developed within a QbD Biopharma 4.0 approach can ensure the quality and consistency of the manufacturing process. This study highlights the importance of continuous LNP formation through microfluidic mixing in ensuring high-quality, in-specification production. Both empty and nucleic acid-loaded LNPs are characterized, followed by a TFF/buffer exchange to obtain process parameters for the envisioned continuous SPTFF. It is shown that LNP generation by pipetting leads to a less preferable product when compared to continuous mixing due to the heterogeneity and large particle size of the resulting LNPs (86–104 nm). Particle size by continuous formation (71 nm) and the achieved encapsulation efficiency (EE) of 88% is close to the targeted parameters for Pfizer’s mRNA vaccine (66–93 nm, 88%EE). With the continuous encapsulation of nucleic acids in LNPs and the continuous production of mRNA in in vitro transcription, the basis for the holistic continuous production of mRNA is now established. We already showed that a fully autonomous process requires the incorporation of digital twins and a control strategy, with predictive process models and state-of-the-art PAT enabling real-time-release testing. This autonomous control can considerably improve productivity by about 15–20% and personnel as well as chemical reduction of about 30%. The results of this work complement this, laying the basis for fully continuous, bottleneck-free production of mRNA and other cell- and gene-therapeutic drug/vaccine candidates in a GMP- and QbD-compliant Biopharma 4.0 facilities on a flexible scale. Full article

Pharmaceutical manufacturing processes are moving towards automation and real-time process monitoring with the help of process analytical technologies (PATs) and predictive process models representing the real system. In this paper, we present a digital twin developed for an adjuvant manufacturing process involving a microfluidic formation of lipid particles. The twin uses a hybrid model for estimating the current state of the process and predicting system behavior in real time. The twin is used to control the adjuvant particle size, a critical quality attribute, by varying process parameters such as the temperature and inlet flow rates. We describe steps in the design and implementation of the twin, starting from the conception of the mechanistic model, up to the generation of its surrogate model used as state estimator, PATs and the setup of the information technology—Operational technology architecture. We demonstrate the performance of the twin by introducing different disturbances in the process and comparing the effect on the product critical quality attributes with and without the control of the digital twin. Finally, we showcase the digital twin implementation for the process in good manufacturing practice, through an engineering run, which demonstrated the robustness of the process when controlled by the digital twin. Full article