Industrial Processes Research Articles

Cultivating sustainability: a multi-assessment of groundwater quality and irrigation suitability in the arid agricultural district of Dzira (Ksour Mountains, Algeria).

Groundwater serves a range of essential functions such as supplying drinking water, facilitating agricultural practices, and supporting industrial processes. This study examines with multiple methods the quality of groundwater in the agricultural region of Dzira, Algeria. By collecting 38 groundwater samples of different wells and boreholes, valuable awareness of the aptness of groundwater for irrigation in this arid landscape was gained. Most wells met Food and Agriculture Organization (FAO) criteria for the total dissolved solids (TDS) and the potential of hydrogen pH, but some areas had higher mineral content and electrical conductivity. Results show significant TDS variations, with 10.81% of wells exceeding limits and acceptable pH levels. Elevated EC values in 67.57% of wells show high salinity, affecting soil and plant growth. Major ions such as Mg2+ and SO4- exceeded FAO standards in 43.24% and 64.86% of wells, respectively, highlighting substantial mineral content in the groundwater. Suitability indices reveal that most wells pose low sodium hazards and are generally suitable for irrigation, though some areas face moderate to high restrictions. The irrigation water quality index (IWQI) ranged from 45.36 to 96.30, averaging 80.77, with 54.04% classified as "low restriction," suitable for sandy soils with good permeability but requiring caution on salt-sensitive soils. Hydrogeochemical analysis using principal component analysis (PCA) and hierarchical cluster analysis (HCA) identifies rapid evaporite dissolution from Triassic saline formations, with a correlation matrix showing associations between TDS and Ca2⁺, Mg2⁺, Na⁺, Cl⁻, and SO₄2⁻. This mineralization is likely from gypsum and halite. Zoning maps based on IWQI and other parameters depicted spatial variations in groundwater quality, guiding effective irrigation management strategies. Overall, the study underscores the importance of comprehensive water quality assessment for sustainable agriculture and emphasizes the need for targeted interventions to mitigate potential challenges associated with soil salinity and sodicity. Therefore, these findings can be useful to decision-makers and stakeholders in order to optimize water use and protect this vital resource.

Bayesian modeling and optimization for split-plot experiments with multiple responses

In many industrial processes, cost or time constraints make some input variables harder to change or control than others. An appropriate experimental design method is restricted randomization, which results in split-plot experiments. Empirical models that connect multiple quality characteristics with input variables play a crucial role in robust parameter design for split-plot experiments. At present, many modeling methods typically adopt the single response model for analyzing industrial processes in the split-plot experiments without considering correlation among multiple responses, correlation among whole plots, and uncertainty of model parameters. However, ignoring these issues can lead to poor product or process design. To solve these issues, this paper suggests a novel Bayesian modeling and optimization approach. We first construct a Bayesian multi-response linear mixed-effects model and obtain the posterior distribution for model parameters by employing Bayesian theorem. Then, the Gibbs sampling procedure is employed for the estimation of model parameters. Finally, the overall weighted desirability optimization function meeting the specification is developed to avoid acquiring ideal input settings with outliers. A simulation and an engineering case study demonstrate the validity of the proposed method. In comparison to existing methods, the optimization results given the proposed method are more robust and reliable.

Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.

A new denitrification strategy based on high-temperature catalytic coatings Y2O3-BaO-ZrO2 without using ammonia in a lab-scale natural gas combustion furnace

Catalytic decomposition of NO at high temperatures without the use of ammonia, offers an eco-friendly and sustainable solution for denitrification in industrial combustion or heating processes. To simulate a complex combustion flue gas, we designed a lab-scale natural gas industrial combustion furnace specifically for assessing the impacts of Y2O3-BaO-ZrO2 catalyst coated on SiC plates on NO emissions. Various factors including the plate number, excess air ratio, combustion power, and coating position were considered. Results demonstrated that arranging the Y2O3-BaO-ZrO2 coated plates shifted the high-temperature zone of the furnace downstream. When the excess air ratio was below 1.05, the catalytic coating significantly reduced NO emission concentration at the furnace outlet by over 40 %. However, this reduction coincided with a substantial presence of unburned CO in flue gases. Placing the coated SiC plates in the constant temperature zone of 1100–1200 °C within the middle of furnace offered significant advantages in NO removal, particularly as the combustion power increased. Based on a 60-h durability evaluation of Y2O3-BaO-ZrO2 coating, the observed good high-temperature thermal and chemical stabilities further enhanced the coating’s potential for industrial application. The possible mechanism of the reduction NO by CO over coating surfaces were analyzed by in situ optical measurements. After calculation, using 12 kg Y2O3-BaO-ZrO2 catalysts might decrease at least 642 kg of ammonia consumption under ideal conditions, and significantly reduce the investment cost on existing flue gas denitrification equipment. Therefore, the investigated technology is a green and sustainable denitrification strategy, which would potentially provide a novel approach towards attaining ultra-low NO emissions.

Synthesis of a Zn(II)-2-aminoimidazole Framework as an Efficient Carbonic Anhydrase Mimic.

Carbonic anhydrase (CA) plays a crucial role in the CO2 capture processes by catalyzing the hydration of CO2. In this study, we synthesized a bioinspired carbonic anhydrase Zn-MOF (metal-organic framework) incorporating 2-aminoimidazole and Zn2+ as initial constituents. The synthesized Zn-MOF exhibited promising potential for efficiently catalyzing the CO2 hydration. Structural analyses such as SEM, XRD, and BET confirmed that the Zn-MOF crystal consisted of stacked grains with an average size of approximately 36 nm, forming a micron-sized spherical structure. Functionally, Zn-MOF exhibited effective catalytic activity toward both CO2 hydration and ester hydrolysis. The introduction of amino groups significantly enhanced the esterase activity of Zn-MOF to 0.28 U/mg at ambient temperature, which was twice that of ZIF-8. Furthermore, the introduction of amino groups resulted in remarkable hydrothermal stability, with the esterase activity reaching 0.72 U/mg after undergoing hydrothermal treatment at 80 °C for 12 h. Additionally, Zn-MOF exhibited enhanced capability in CO2 hydration at a pH value exceeding 8.5. After six repeated uses, ZIF-8 and Zn-MOF retained approximately 68 and 65% of their initial enzyme activity, respectively, underscoring the potential practical applicability of Zn-MOF in industrial CO2 capture processes. This work showcases the development of a novel Zn-MOF crystal as an efficient CA mimic, effectively emulating the active sites of natural CA using 2-aminoimidazole as a coordinating ligand for Zn2+ coordination. These findings not only advance the field of innovative enzyme mimics but also pave the way for further exploration of industrial CO2 capture catalysts.

Dairy Fermentation 2.0

The manufacture of fermented dairy products has a long history, and such products are produced globally, following artisanal or industrial fermentation processes, satisfying consumers’ need for healthy, nutritious, delicious and safe foods [...]

Integrating supervised and unsupervised learning approaches to unveil critical process inputs

This study introduces a machine learning framework tailored to large-scale industrial processes characterized by a plethora of numerical and categorical inputs. The framework aims to (i) discern critical parameters that influence the output and (ii) generate accurate out-of-sample qualitative and quantitative predictions of production outcomes. Specifically, we address the pivotal question of the significance of each input in shaping the process outcome, using an industrial Chemical Vapor Deposition (CVD) process as an example. The initial objective involves merging subject matter expertise and clustering techniques exclusively on the process output, here, coating thickness measurements at various positions in the reactor. This approach identifies groups of production runs that share similar qualitative characteristics, such as film mean thickness and standard deviation. In particular, the differences of the outcomes represented by the different clusters can be attributed to differences in specific inputs, indicating that these inputs are potentially critical to the production outcome. Shapley value analysis corroborates the formed hypotheses. Leveraging this insight, we subsequently implement supervised classification and regression methods using the identified critical process inputs. The proposed methodology proves to be valuable in scenarios with a multitude of inputs and insufficient data for the direct application of deep learning techniques, providing meaningful insights into the underlying processes.

Selective and sensitive molecularly imprinted polymer-based electrochemical sensor for detection of deltamethrin

Electrochemical sensors have a broad range of industrial applications due to their sensitivity, speed, and cost-effectiveness. These sensors enable the continuous monitoring and control of critical parameters in various industrial processes. For instance, they are essential in food safety, environmental monitoring, biomedical applications, and pharmaceutical production. In the food industry, electrochemical sensors facilitate the rapid and reliable detection of contaminants and pathogens in food products, thus enhancing product quality and consumer safety. An electrochemical sensor was developed with the molecularly imprinted polymer (MIP) technique to detect deltamethrin with high sensitivity and selectivity. The sensor was fabricated by electrodeposition of Co3O4 on indium tin oxide (ITO), followed by electropolymerization of o-phenylenediamine with deltamethrin as a template molecule. The template molecules were then removed from the modified electrode by a methanol. The MIP-based electrochemical sensor exhibited high sensitivity and selectivity towards deltamethrin. Under the optimized conditions, the LOD values for the MIP/Co3O4/ITO electrode in the first and second linear regressions were 1.53 nM for linear range of 2.82 nM to 56.5 nM and 0.34 μM for linear range of 0.25 μM to 3.98 μM. Moreover, the LOD values for the NIP/Co3O4/ITO electrode in the first and second regressions were 2.43 nM for the linear range of 3.91 nM to 65.0 nM and 726.0 nM for the linear range of 0.023 μM to 4.5 μM. The developed electrochromic pesticide sensor, being an electrochemical-based molecularly imprinted polymer (MIP) sensor incorporating electrochromic materials, enables both target-specific pesticide detection and visual pesticide identification based on color changes dependent on pesticide concentration. Consequently, this system is more advantageous compared to electrochemical-based MIP sensors, as it provides both qualitative and quantitative determinations. The qualitative assessment aims to enhance the ease of use of the sensor, thereby increasing the potential for it to become a commercially viable product by reducing the need for instrumental devices.

Flexible and Ecological Cotton-Based Dosimeter for 2D UV Surface Dose Distribution Measurements.

This work presents a 2D radiochromic dosimeter for ultraviolet (UV) radiation measurements, based on cotton fabric volume-modified with nitroblue tetrazolium chloride (NBT) as a radiation-sensitive compound. The developed dosimeter is flexible, which allows it to adapt to various shapes and show a color change from yellowish to purple-brown during irradiation. The intensity of the color change depends on the type of UV radiation and is the highest for UVC (253.7 nm). It has been shown that the developed dosimeters (i) can be used for UVC radiation dose measurements in the range of up to 10 J/cm2; (ii) can be measured in 2D using a flatbed scanner; and (iii) can have the obtained images after scanning be filtered with a medium filter to improve their quality by reducing noise from the fabric structure. The developed cotton-NBT dosimeters can measure UVC-absorbed radiation doses on objects of various shapes, and when combined with a dedicated computer software package and a data processing method, they form a comprehensive system for measuring dose distributions for objects with complex shapes. The developed system can also serve as a comprehensive method for assessing the quality and control of UV radiation sources used in various industrial processes.

Influence of Rotation and Viscosity on Parallel Rolls of Electrically Conducting Fluid

Rayleigh–Bénard convection is a fundamental fluid dynamics phenomenon that significantly influences heat transfer in various natural and industrial processes, such as geophysical dynamics in the Earth’s liquid core and the performance of heat exchangers. Understanding the behavior of conductive fluids under the influence of heating, rotation, and magnetic fields is critical for improving thermal management systems. Utilizing the Boussinesq approximation, this study theoretically examines the nonlinear convection of a planar layer of conductive liquid that is heated from below and subjected to rotation about a vertical axis in the presence of a magnetic field. We focus on the onset of stationary convection as the temperature difference applied across the planar layer increases. Our theoretical approach investigates the formation of parallel rolls aligned with the magnetic field under free–free boundary conditions. To analyze the system of nonlinear equations, we expand the dependent variables in a series of orthogonal functions and express the coefficients of these functions as power series in a parameter ϵ. A solution for this nonlinear problem is derived through Fourier analysis of perturbations, extending to O(ϵ8), which allows for a detailed visualization of the parallel rolls. Graphical results are presented to explore the dependence of the Nusselt number on the Rayleigh number (R) and Ekman number (E). We observe that both the local Nusselt number and average Nusselt number increase as the Ekman number decreases. Furthermore, the flow appears to become more deformed as E decreases, suggesting an increased influence of external factors such as rotation. This deformation may enhance mixing within the fluid, thereby improving heat transfer between different regions.

Studies of Wastewater Management through Microbiology of Constructed Wetland

Constructed wetlands are distinguished by certain environmental factors that let many physical and biological processes to occur simultaneously. This is the outcome of a particular habitat that supports the development of microbes and hydrophytes, which are plants that can live in aquatic and semiaquatic environments and are capable of anaerobic, anaerobic, and aerobic environments. Their combined activity amplifies the processes of oxidation and reduction, which are in charge of eliminating and holding onto contaminants. Sorption, sedimentation, and absorption all aid in these processes. Treatment wetland systems have been employed in community management for more than 50 years because of these benefits. Constructed wetlands are becoming more and more popular for treating or pre-treating many kinds of industrial wastewater because of their benefits, cheap operating costs, and high removal efficiency. The paper examines how these facilities are being used worldwide to treat industrial wastewater. With a focus on particular and distinctive pollutants from industries, the conditions of usage and efficacy of pollutants removal from wastewater that degrades quickly and slowly were described. The article addressed the use of subsurface horizontal flow beds for the treatment of wastewater from various industrial processes, including the manufacturing of paper, oil, and coffee, as well as the food sector, which includes wineries and distilleries. Constructed wetlands are used in Poland to treat wastewater and milk processing sludge on a pilot size or to dewater sewage sludge generated in municipal wastewater treatment plants that handle household sewage with around 40% of wastewater from the dairy and fish industries. Constructed wetlands provide the so-called ecosystem function in addition to an adequate degree of treatment in every instance.

Integrated Platform for Determining Solderability Parameters: Module for Measuring the Surface Tension of Liquid Solders

This article describes a module and method for measuring the surface tension of liquid solders implemented on a measuring device as part of an integrated platform for automatic measurement of brazebility parameters at high temperatures. A concept for constructing a test stand is presented, with a description of the individual functional blocks. The developed stand allows for testing of the solder’s surface tension. The surface tension is one of the parameters that describe the thermodynamics of interfacial reactions and the structure of newly created joints. Determining the physicochemical interactions between liquid and solid substances is crucial for various industrial processes in fields such as metallurgy, electronics, and aviation, mainly where soldering and brazing technologies are employed. A series of bubble experiments in solder for a ceramic capillary is carried out to verify the proposed method using the developed system. One of these experiments is described in this article.

Dual-Path Neural Network based on Deep Features and Transfer Learning for Bearing Fault Diagnosis

The safe operation of mechanical equipment is a basic requirement in the industrial manufacturing process. As an important component of mechanical equipment, rolling bearings are prone to unforeseen failures due to long-term operation under complex conditions. The occurrence of failures, no matter how big or small, will cause economic losses. Therefore, reliable diagnosis of rolling bearings is crucial. Aiming at the problem of insufficient feature recognition of convolutional neural networks under strong noise background, a deep feature extraction network integrating multiscale convolutional neural network (MSCNN) and bidirectional gated recurrent unit (BiGRU) is proposed. MSCNN and BiGRU are used to extract multiscale features and temporal features from noisy vibration signals respectively, and different weights are assigned to the fused features through the attention mechanism module to achieve important feature selection. Furthermore, in order to solve the problem of different feature distributions between the source domain and the target domain under variable working conditions, transfer learning is introduced in the proposed deep feature extraction network. The difference in feature distribution between the source domain and the target domain is measured by a multi-level distance formula, and the measurement result is added to the loss function. The back propagation of the loss is used to achieve the alignment of feature distribution between the source domain and the target domain. Finally, the model uses the SoftMax function as a classifier for rolling bearing fault diagnosis. Experimental comparison and analysis show that the proposed model has good migration ability and achieves a higher fault identification accuracy.

Experimental Studies on the Siirt Herby Tulum Cheese: II. Evaluation of a New Industrial Process Model

This study was conducted to test a process that we had previously developed in parallel to the traditional Siirt Herby Cheese production method. Both raw and pasteurized Eve's milk were used parallelly in the study. Pasteurized milk was inoculated with an autochthonous starter culture which we have developed. After clot formation, breaking the clot, straining and acidification of curd by using acid whey, first pressing, adding herb and salt, and applying the second pressing stages were followed. Then, the cheese samples were packaged. No air gap was presented in the cheese containers. The entire production was completed within 24 hours. During the 120-d ripening period of the samples at 4 °C, pH was observed to be 5 and acidity was 0.7% (lactic acid). In raw milk cheese, pH was 6.8 and acidity was 1.12% at the end of the ripening period. It was determined that the method tested in this study was not recommendable for making raw milk cheese. The pasteurized milk cheese samples had at least 0.7 acidity, 5 pH, 20% fat and 20% protein; It was observed that at least 45% dry matter values could be obtained. However, the pasteurized milk cheese samples did not fully meet our expectations. The crumbling property of the cheese samples was not ideal just seen in the tradiditonal Tulum Cheeses of Türkiye. The slightly sticky and melted appearance was considered a negative property of the cheese and should be eliminated with more detailed work. Traditional production takes at least 10 d. This period may be long for industrial production. Raw milk is used in traditional production, and excessive salt is added to the cheese for hygiene purposes. Also, it is not easy to make a standard production. More research is needed to eliminate such negativities, and to recommend a valuable industrial process.

Future Parabolic Trough Collector Absorber Coating Development and Service Lifetime Estimation

This work presents a study on the optical and mechanical degradation of parabolic trough collector absorber coatings produced through the spray coating application technique of in-house developed paint. The main aim of this investigation is to prepare, cure, load, and analyze the absorber coating on the substrate under conditions that mimic the on-field thermal properties. This research incorporates predicted isothermal and cyclic loads for parabolic trough systems as stresses. Biweekly inspections of loaded, identical samples monitored the degradation process. We further used the cascade of data from optical, oxide-thickening, crack length, and pull-off force measurements in mathematical modelling to predict the service life of the parabolic trough collector. The results collected and used in modelling suggested that cyclic load in combination with iso-thermal load is responsible for coating fatigue, influencing the solar absorber optical values and resulting in lower energy transformation efficiency. Finally, easy-to-apply coatings made out of spinel-structured black pigment and durable binder could serve as a low-cost absorber coating replacement for a new generation of parabolic trough collectors, making it possible to harvest solar energy to provide medium-temperature heat to decarbonize future food, tobacco, and paint production industrial processes.

Potential improvement in thermoelectric properties of SnTe polycrystals via anionic and cationic substitution

Heat is produced by all machinery, including microprocessors and jet engines, as well as by industrial processes that produce goods from steel to food. There is still a strong desire for alternative energy sources in this day of ecologically conscious and sustainable energy needs. The recovery of heat from waste heat into beneficial electrical energy provides a simple energy source with enormous potential. Thermoelectrics is one of the popular technologies, which can convert heat into electricity, making them useful for power generation. Tin telluride (SnTe), a significant type of newly developed thermoelectric material, has drawn a lot of attention due to its low toxicity and environmentally friendly characteristics. Here, we synthesize Bi/Se co-doped SnTe (Sn1-xBixTe0.97Se0.03 (x = 0, 0.02, 0.04, 0.06)) samples by employing the solid-state metathesis route. The results of this work suggest that the combined action of cationic and anionic substitution of Bi and Se to SnTe matrix could potentially modify the microstructure and band structure of SnTe, thereby effectively improving its electronic, mechanical, and thermal transport properties. In line with the experimental findings, the samples show degenerate semiconducting electronic transport behaviour throughout the temperature range. The maximum Seebeck coefficient is found to be ∼84 μV/K and the total thermal conductivity is reduced to 1.6 W/mK at 573 K in a 6 % Bi-doped sample, which is 2.2 times less than the pristine sample. The Sn0.94Bi0.06Te0.97Se0.03 sample has a maximum ZT of approximately 0.23 at 573 K, which is three times higher than the pristine sample because of the combined regulation of cation-anion co-doping and an elevated power factor of 936 μW/K2m in Sn0.96Bi0.04Te0.97Se0.03 sample at 573 K, which is 1.82 times higher than that of pristine SnTe. Considering these findings, it appears that Bi-Se co-doped SnTe is a promising material for thermoelectric applications.

Liquid-liquid equilibrium of binary ethanol–polyisobutene and ternary ethanol–water–polyisobutene systems under low- and high-pressure conditions: An assessment using the PC-SAFT equation of state

Sugarcane bioethanol in either anhydrous or hydrous forms stands out as an economically viable bioproduct that can be used as a fuel or feedstock in various innovative applications, reducing human-generated greenhouse gas emissions. Low-molar-mass polyisobutene (PiB) is used as a lubricant in sugarcane-processing plants and is a possible contaminant of sugarcane bioethanol. Understanding the liquid–liquid equilibrium (LLE) of ethanol–PiB binary systems and ethanol–water–PiB ternary systems is essential to avoid undesired phase separation at high-pressure engine conditions and other potential innovative applications. However, the experimental investigation of these systems is significantly hampered by very low polymer solubilities in aqueous ethanol. The LLE of ethanol-PiB and ethanol–water–PiB mixtures was qualitatively investigated using the PC-SAFT equation of state through the development and implementation of extrapolation strategies allowing to estimate ethanol–PiB and water–PiB binary parameters using experimental data obtained for other systems containing lighter alkanes. Phase diagrams were calculated considering temperatures ranging from 298.15 K to 350 K and pressures ranging from 1 bar to 200 bar. It was shown that lower pressures, higher temperatures, lower PiB molar masses, and lower water contents increase the polymer solubility in the solvents. Thus, the risk of liquid–liquid separation can be mitigated by correctly managing such operational parameters. The obtained phase diagrams may present significant quantitative deviations from reality because of the model’s sensitivity to the estimated binary parameters. Thus, they should be interpreted as general qualitative guidelines to avoid and manage undesired phase separation in industrial processes and engines rather than phase equilibrium predictions.

Analytical Study on Reducing the Heating Effects of Daylight and Improving Natural Lighting Performance at Erzincan Train Station in the Context of Climate Change

Climate change is an environmental issue that is rapidly escalating due to the effects of global warming. The increase in carbon emissions, along with various human activities such as industrial processes, land use changes, and the reckless consumption of natural resources, are among the primary causes of global warming. Various architectural interventions are being implemented in historical buildings to mitigate the impact of global warming and its consequence, climate change. The study aims to protect the historical building from the harmful effects of climate change by reducing the heating effects of direct sunlight during the summer and enhancing the efficiency of natural lighting throughout the day. In this context, the Erzincan Train Station is located in Erzincan in Eastern Anatolia. Erzincan is one of the most affected by the climate change crisis has been selected for the study. The plans of the Erzincan Train Station were digitized using AutoCAD software and subsequently modeled in three dimensions using Revit software. Daylight analysis was conducted on the created model using the Insight plugin in Revit software. The analysis determined that recommendations should be made for the building's south facade. Based on the climate data of Erzincan province and the conducted analyses, the light shelf system was designed and implemented due to its applicability, shading, and lighting advantages among daylighting systems. The effects of the proposed system were examined using spatial daylight autonomy (sDA) and annual sunlight exposure (ASE) daylight metrics according to LEED v4 EQc7 standards. The study found that the light shelf system resulted in a 5% reduction in ASE values building-wide and a 6% reduction in the average values of specific areas while increasing sDA values. In this context, it has been observed that areas experiencing a decrease in direct sunlight exposure (ASE values) also show an increase in sufficient daylight exposure (sDA values), which could contribute to improving the building's energy performance.

Dispersion in a slit with crossflow filtration through a porous wall

Crossflow filtration is a separation technique where fluid flows tangentially across a membrane or porous media, reducing clogging by sweeping away retained particles and allowing continuous filtration. Dispersion of solute matter in crossflow filtration plays an essential role in the separation performance of many industrial processes. The current work aims to generalize the Taylor dispersion theory and study the solute transport in a slit–porous medium system with a crossflow. The solute is depleted by the porous medium at the slit top porous wall or the interface between the slit and the porous medium, where the continuity of the solute concentration and mass flux is applied. The solute is simultaneously transported axially by the main flow along the slit and vertically by the crossflow perpendicular to the slit. Using the Reynolds decomposition and cross-sectional averaging techniques, the generalized reduced-order model for the advection-dispersion solute transport in the slit–porous medium system with the presence of a crossflow is established, where the effective velocity of the main flow and the effective dispersion coefficient are obtained. The analysis of the results reveals that the nondimensional Taylor dispersion coefficient scales with the Peclet numbers for the crossflow and the main flow, respectively, as DT∼Pev−5/3 (when Pev≥20) and DT∼Peu2. The proposed theoretical model, along with the findings of this study, paves the way for the fundamental study of dispersion in more complex systems.

Vapor-Liquid Equilibrium of System Comprising Green Solvents: A Holistic Review

Abstract: The design and operation of distillation columns is based on vapor-liquid equilibrium data, which is a necessity for the chemical industry. In recent years, chemical industry has embraced green chemistry and sustainable development. Furthermore, green solvents are more environmentfriendly and cleaner than conventional solvents and thus offer a good alternative. Very limited work has been reported in the literature that focuses on the generation of isobaric/isothermal vapor-liquid equilibrium (VLE) data of systems comprising green solvents. In this paper, reported VLE data are explored for three emerging green solvents, such as cyclopentyl methyl ether (CPME), γ- valerolactone (GVL), and 2-methyltetrahydrofuran (2-MeTHF). Emerging green solvents have favorable environmental, health, and safety characteristics, making them attractive alternatives for a wide range of applications. The study focuses on two critical separations; the extraction of formic acid from Power-to-X chemical processes and purification of acetic acid from chemical synthesis or fermentation processes. Both processes are integral parts of the chemical industry's sustainable development. To facilitate these separations, accurate VLE data for these green solvents with acetic acid/formic acid systems are essential. The paper reviews literature related to VLE data for systems involving these green solvents. It provides insights into the experimental conditions, equipment, analysis methods, thermodynamic models, and error-minimizing functions used in the previous studies. The researchers can refer to this information as a useful reference prior to the VLE experimentation and modeling of systems comprising these three green solvents. Moreover, the paper presents an overview of recent research on green solvents and their applications, illustrating their versatility and potential for various industrial processes. Research efforts are needed to generate VLE data for green solvent systems and support the chemical industry in transitions towards more sustainable practices.