Experimental Data Research Articles

T-YOLO: a lightweight and efficient detection model for nutrient buds in complex tea-plantation environments.

Quick and accurate detection of nutrient buds is essential for yield prediction and field management in tea plantations. However, the complexity of tea plantation environments and the similarity in color between nutrient buds and older leaves make the location of tea nutrient buds challenging. This research presents a lightweight and efficient detection model, T-YOLO, for the accurate detection of tea nutrient buds in unstructured environments. First, a lightweight module, C2fG2, and an efficient feature extraction module, DBS, are introduced into the backbone and neck of the YOLOv5 baseline model. Second, the head network of the model is pruned to achieve further lightweighting. Finally, the dynamic detection head is integrated to mitigate the feature loss caused by lightweighting. The experimental data show that T-YOLO achieves a mean average precision (mAP) of 84.1%, the total number of parameters for model training (Params) is 11.26 million (M), and the number of floating-point operations (FLOPs) is 17.2 Giga (G). Compared with the baseline YOLOv5 model, T-YOLO reduces Params by 47% and lowers FLOPs by 65%. T-YOLO also outperforms the existing optimal detection YOLOv8 model by 7.5% in terms of mAP. The T-YOLO model proposed in this study performs well in detecting small tea nutrient buds. It provides a decision-making basis for tea farmers to manage smart tea gardens. The T-YOLO model outperforms mainstream detection models on the public dataset, Global Wheat Head Detection (GWHD), which offers a reference for the construction of lightweight and efficient detection models for other small target crops. © 2024 Society of Chemical Industry.

Kinetics Modeling of Biogas Production from Market Waste Inoculated with Abattoir Waste

The anaerobic digestion process is a waste management method that is driven by microbes and therefore, it’s vital to understand the basic operation kinetics. This study demonstrates the kinetic study for twenty market wastes inoculated with rumen fluid at mesophilic temperature. The substrates consisted of blended market wastes inoculated with rumen fluid for a seven days hydraulic retention time. The experimental data obtained were used for kinetic studies by fitting the data to Linear, Exponential, Gaussian, Logistics, and Modified Gompertz kinetic models. The results obtained showed that high cumulative biogas was observed in the market wastes mixed sample at 3500 mL followed by sweet potato, potato, and banana wastes at 2000 mL and 1700 mL respectively. The un-inoculated wastes fruit and vegetable wastes mixtures produced 300 mL, blank rumen 700 mL while co-digestion of waste with rumen matter produced 3500 mL of biogas. The kinetic evaluation of the biogas generation data showed that the coefficient of determination (R2) was in the following ranges for all the twenty market wastes, linear model: 0.5478 - 0.9973, exponential model: 0.9099 - 0.9984, Gaussian model: 0.879-0.9932, Logistic Growth model: 0.9602 – 0.9963 and Modified Gompertz model: 0.9987 – 0.9999 respectively. Therefore, the Modified Gompertz model yielded high-accuracy result. Further, biogas generation from these models showed high accuracy with 25.96 mL/g cumulative biogas in contrast to the experimental yields of 23.58 mL/g with slight deviations of 2.87 %.

Targeted optimization in small-scale atomic structure calculations: application to Au I

The lack of reliable atomic data can be a severe limitation in astrophysical modelling, in particular of events such as kilonovae that require information on all neutron-capture elements across a wide range of ionization stages. Notably, the presence of non-orthonormalities between electron orbitals representing configurations that are close in energy can introduce significant inaccuracies in computed energies and transition probabilities. Here, we propose an explicit targeted optimization (TO) method that can effectively circumvent this concern while retaining an orthonormal orbital basis set. We illustrate this method within the framework of small-scale atomic structure models of Au I, using the Grasp2018 multiconfigurational Dirac–Hartree–Fock atomic structure code. By comparing to conventional optimization schemes we show how a TO approach improves the energy level positioning and ordering. TO also leads to better agreement with experimental data for the strongest E1 transitions. This illustrates how small-scale models can be significantly improved with minor computational costs if orbital non-orthonormalities are considered carefully. These results should prove useful to multi-element atomic structure calculations in, for example, astrophysical opacity applications involving neutron-capture elements.

Aggregate Type and Concrete Age Effects on Anchor Breakout Performance: Large Database and Insights

This contribution summarizes the largest available literature data collection on tensile and shear loaded anchor tests, obtained in two independent studies and performed by two different research groups. It was the objective of the two studies to investigate a possible effect that petrographically different coarse aggregate types may have on the tensile and shear load capacity for concrete breakout failure modes. In total, seven normal-strength and four high-strength concretes were tested at two different ages. Structural tests were performed on cast-in (tensile and shear tests) and post-installed adhesive anchors (shear tests). Parallel to the structural tests, each concrete was characterized in terms of compressive and tensile strength. Finally, the combined experimental data offer novel insights into the predictive quality of available design models for concrete cone capacity in tension and edge breakout in shear with respect to a potential aggregate effect. Systematic analyses indicate only minor aggregate effects after normalization by compressive strength (less than 7% difference between normalized values). However, the study reveals potential curing and concrete age effects where a 9% increase in predicted values is shown when concrete cures longer. The predictive equations remain conservative in comparison to all the investigated properties and their validity is shown in this study.

Municipal Plastic Waste Recycling through Pyrogasification

Conventional mechanical recycling technologies cannot recycle all types and amounts of generated plastic waste. Pyrolysis can convert these municipal mixed plastic streams into products with significant calorific value, which are likely to be used as energy sources. The present work describes a technology used to expand the portfolio of technical approaches to drive plastics circularity, i.e., thermochemical recycling. A base case scenario considered a capacity of 1.000 kg/h of municipal plastic waste, consisting of a mixture of polypropylene (PP), polystyrene (PS), polyethylene (PE), and plastic associated with paper, which were converted into non-condensable gases, oil, and char through a pyrogasification system. Based on mass and energy balances and experimental data from the literature, a total of 199.4 kg (48 MJ/kg) of liquid fuel and 832.85 kg (16 MJ/kg) of gas could be obtained with no need for external heating sources. The thermal requirement for the pyrolysis of 1.000 kg of municipal plastic waste (1.316 MJ) was supplied by the gasification of a fraction of the produced pyrolysis oil and gases. This feasibility analysis confirmed the technical adequacy of the proposed technology, which that will be further complemented by a technoeconomic study of the proposed solution.

One-vs-One, One-vs-Rest, and a novel Outcome-Driven One-vs-One binary classifiers enabled by optoelectronic memristors towards overcoming hardware limitations in multiclass classification

Deep neural networks have achieved considerable success over the past ten years in a variety of fields. However, current state-of-the-art artificial intelligence (AI) systems require large computing hardware infrastructure and high power consumption. To overcome these hurdles, it is required to adopt new strategies such as designing novel computation architectures and developing building blocks that can mimic the low energy consumption of biological systems. On the architecture level, implementing classification tasks by splitting the problem into simpler subtasks is a way to relax hardware constraints despite the less accuracy of the approach. On the computation unit level, memristive devices are a promising technology for low power neuromorphic computation. Hereby, we combine both these two approaches and present a novel algorithmic approach for multiclass classification tasks through splitting the problem into binary subtasks while using optoelectronics memristors as synapses. Our approach leverages the core fundamentals from the One-vs-One (OvO) and the One-vs-Rest (OvR) classification strategies towards a novel Outcome-Driven One-vs-One (ODOvO) approach. The light modulation of synaptic weights, fed in our algorithm from experimental data, is a key enabling parameter that permits classification without modifying further applied electrical biases. Our approach requires at least a 10X less synapses (only 196 synapses are required) while reduces the classification time by up to N2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\frac{N}{2}$$\\end{document} compared to conventional memristors. We show that the novel ODOvO algorithm has similar accuracies to OvO (reaching over 60% on the MNIST dataset) while requiring even fewer iterations compared to the OvR. Consequently, our approach constitutes a feasible solution for neural networks where key priorities are the minimum energy consumption i.e., small iterations number, fast execution, and the low hardware requirements allowing experimental verification.

Life cycle assessment of n-caproic acid production via chain elongation from food waste: Comparison of shunting and staged technology

n-Caproic acid is a widely used biochemical that can be produced from organic waste through chain elongation technology. This study aims to evaluate the environmental impacts of n-caproic acid production through chain elongation by two processes (i.e., shunting and staged technology). The Open-life cycle assessment (LCA) model was used to calculate the environmental impacts of both technologies based on experimental data. Results showed that the shunting technology had higher environmental impacts than the staged technology. Water and electricity made bigger contribution to the environmental impacts of both technologies. Reusing chain elongation effluent substituting for water and using electricity produced by wind power could reduce the environmental impacts of water and electricity effectively. Using ethanol from food waste had higher global warming potential than fossil ethanol, which suggested that a cradle-to-grave LCA is needed to be carried out for specific raw materials and chain elongation products in the future.

Modelling and dynamic simulation to produce fermentable sugars from lignocellulosic substrates through dilute acid hydrolysis

AbstractThis research paper presents a comprehensive investigation aimed at enhancing the 2G bioethanol production process through the implementation of a dynamic process simulator. The simulator, developed using the Julia programming language, enables the prediction of acid hydrolysis behaviour by manipulating critical variables, including liquid–solid ratio (LSR), acid concentration (CA), and processing time (t). Through meticulous simulations and subsequent experimental validation, optimal operating conditions were revealed, with an H2SO4 concentration of 2% v/v, a LSR of 4% v/w, and 60‐min processing time at 121.1°C. This configuration led to remarkable outcomes, including a xylose concentration of 47.45 g L−1 and an 87.4% hemicellulose removal percentage. Moreover, the simulator unveiled the adverse influence of low LSR values on xylose production and the generation of degradation products. The recalibration of kinetic parameters, guided by experimental data, further fine‐tuned the simulator's predictive accuracy. Overall, this study underscores the potential of the simulator in optimizing various raw materials and presents a promising avenue for advancing 2G bioethanol industry practices.

Steam cracking of sulfur containing compounds: A fundamental modelling study

Sulfur-containing additives are commonly used in industrial steam cracking to mitigate carbon monoxide (CO) formation. Their impact on solid coke deposition in reactors is well-studied but not fully understood. This study investigates the decomposition of five common sulfur compounds (DMDS, DMS, DMSO, CS2, COS) in both inert and hydrocarbon environments within the steam cracking industry. To achieve this, an in-house automated network generation tool called Genesys and a validated ab initio methodology at the CBS-QB3 level are used. This combination generates a comprehensive kinetic model with precise thermodynamic and kinetic parameters. The kinetic model is rigorously validated against experimental data from prior studies under various conditions, including inert nitrogen atmospheres and hydrocarbon matrices (using ethane and heptane). Experimental setups involve bench-scale annular quartz reactors and pilot-scale stainless steel reactors. Remarkably, the kinetic model accurately predicts primary products and captures secondary product trends across diverse conditions without parameter adjustments. Rate of production analyses reveal critical decomposition pathways during sulfur-containing additive pyrolysis and the influence of hydrocarbon matrices. Notably, these additives primarily yield H2S upon decomposition, with CS2, SO2 and thiophene as minor products. In conclusion, the developed fundamental kinetic model accurately predicts product formation during the pyrolysis of DMS, DMDS, DMSO, CS2 and COS, whether in nitrogen or a hydrocarbon matrix, without altering core thermodynamic and kinetic parameters.

Equilibrium solubility, solvent effect, and equation correlations of cyclosporine in twenty mono solvents and four binary mixtures

In this study, the solid–liquid equilibrium solubility of cyclosporine in 20 pure solvents and four binary mixtures at 298.15 K were determined using shake-flask technique. Solid-state characterization exhibited that no solvate formation or polymorphic transformation occurred during the dissolution of cyclosporine in any of the solvent systems investigated. The experimental solubilities of cyclosporine in 20 pure mono solvents were ranked as follows: DEGME (2.21 × 10−1) > ethanol (1.22 × 10−1) > NMP (1.15 × 10−1) = 1-butanol (1.15 × 10−1) > dichloromethane (1.07 × 10−1) > acetonitrile (1.02 × 10−1) > PEG 200 (9.78 × 10−2) > PEG 300 (9.12 × 10−2) > chloroform (8.98 × 10−2) > PEG 400 (8.11 × 10−2) > DMF (7.65 × 10−2) > 2-propanol (7.09 × 10−2) > 1-propanol (6.57 × 10−2) > methanol (6.14 × 10−2) > propylene glycol (6.08 × 10−2) > tetrahydrofuran (4.69 × 10−2) > DMSO (2.64 × 10−2) > acetone (1.28 × 10−2) > glycerol (1.35 × 10−5) > water (4.29 × 10−7). In the four binary solvents (DEGME + water, PEG 200 + water, PEG 300 + water, and PEG 400 + water), the mole fraction solubility of cyclosporine increased with increasing mass fractions of the four different organic solvents. Based on the Kamlet-Taft linear solvation energy relationship (KAT-LSER) model, the solubility of cyclosporine was mainly affected by the self-cohesiveness of the solvent, which is related to the interaction between the solvent and the solvent. Additionally, the solubility parameter was used to explain and understand the solubility of cyclosporine in four different binary solvents. To correlate the solubility data of cyclosporine, two models (the Yalkowsky Log-Linear and simplified CNIBS/R–K models) were applied. The calculated data based on two mathematical models and experimental mole fraction solubility data for cyclosporine could provide information for the separation, extraction, purification, crystallization, and selection of suitable a solvent system.

Insights into Agricultural Soil Contamination by a Veterinary Antibiotic TYLOSIN Through Continuous Run-Off Conditions

ABSTRACT Broad-spectrum veterinary antibiotic tylosin (TYL) is utilized in livestock and poultry. Due of its extensive usage and animal excretion in urine and feces, it affects the environment and human health. For determining the equilibrium time, the adsorption maximum capacity and the optimal isotherm model, adsorption batch tests have been conducted. Sips and Pseudo-second order models had R2 values of 0.9937 and 0.9764, respectively, and best matched experimental isotherm and kinetic data. The maximum TYL uptake onto soil was 2355.08 mg.kg−1. Using fixed bed adsorption experiments, the current study clarifies the issue of soil contamination in conditions that are close to natural phenomena. Fixed bed adsorption tests demonstrated that the inlet TYL concentration, soil bed height, and flow rate, influenced saturation and breakthrough times. Bohart-Adams, Thomas, and Yoon-Nelson models matched the experimental findings well in a variety of conditions. With 100 mg.L−1 concentration, 4 cm bed height, and 5 mL.min−1 flow rate, the maximal bed capacity of 1277.06 mg.kg−1 was achieved with R2 value of 0.9797. Experimental findings also indicated that the adsorption capacity in batch and dynamic tests are of the same range of magnitude. Based on this, tylosin is a probable soil contaminant.

Recent Upgrade of the Magnetic Diagnostic System in the Alvand-U Tokamak

The main acting parameters in tokamak plasma experiments, such as currents and electric and magnetic fields, are present inside and outside the plasma volume. To analyze the changes in the shape of tokamak plasma, it is necessary to use experimental data obtained from magnetic measurements. Until recently, a four-digit magnetic probe (MP) was used for qualitative analysis of the magnetic field, but its accuracy was low, and it could not be practically used as a diagnostic system. Following the Magnetic Confinement Group, Tokamak Laboratories’ approach to optimize and improve the measurement of plasma parameters, the use of a richer array of MPs to accurately measure changes in the magnetic field and identify plasma parameters in the Alvand tokamak was considered. In order to upgrade the magnetic diagnostic system, two MP arrays were designed and manufactured. Each array contains 12 poloidal and radial probes. The MPs of each array were attached to Mylar belts and mounted on the vacuum vessel of the tokamak. In addition, an integrator unit was designed and installed as a magnetic diagnostic subsystem. The conducted research involved the MP and calibration setup followed by preliminary tests of the array probe.

Elastic-plastic properties of ceramic matrix composites characterization by nanoindentation testing coupled with computer modeling

The nanoindentation test is utilized to measure the hardness and elastic modulus of various components within Ceramic Matrix Composites (CMCs), including continuous stiffness measurement (CSM) and quasi-static testing. To further characterize the elastic-plastic properties of CMCs, an efficient method is proposed by using backpropagation (BP) neural network inversion, which is based on the relationship between load and depth obtained by the nanoindentation experiment and Abaqus finite element simulation. As part of the analysis, the load-depth (p-h) curve is simplified to six characteristic parameters through the fitting of the experimental results. The pre-estimated elastic-plastic characteristics are utilized to model the nanoindentation test for CMCs. A BP neural network is used to perform deep learning on simulation data, which leads to a network structure that matches the trained parameters of the p-h curve and elastic-plastic properties. The experimental data for fiber is inputted into the network to acquire the elastic-plastic properties required in practice. The accuracy of the proposed method is verified by comparing the inversion results with the simulation outcomes.

Data-Based Modeling, Multi-Objective Optimization and Multi-Criteria Decision Making of a Catalytic Ozonation Process for Degradation of a Colored Effluent

In the catalytic ozonation process (COP), the reactions are complex, and it is very difficult to determine the effect of different operating parameters on the degradation rate of pollutants. Data-based modeling tools, such as the multilayer perceptron (MLP) neural network, can be useful in establishing the complex relationship of degradation efficiency with the operating variables. In this work, the COP of acid red 88 (AR88) with Fe3O4 nano catalyst was investigated in a semi-batch reactor and a MLP model was developed to predict the degradation efficiency (%DE) of AR88 in the range of 25 to 96%. The MLP model was trained using 78 experimental data having five input variables, namely, AR88 initial concentration, catalyst concentration, pH, inlet air flow rate and batch time (in the ranges of 150–400 mg L−1, 0.04–0.4 g L−1, 4.5–8.5, 0.5–1.90 mg min−1 and 5–30 min, respectively). Its optimal topology was obtained by changing the number of neurons in the hidden layer, the momentum and the learning rates to 7, 0.075 and 0.025, respectively. A high correlation coefficient (R2 > 0.98) was found between the experimental and predicted values by the MLP model. Simultaneous maximization of %DE and minimization of Fe3O4 concentration was carried out by multi-objective particle swarm optimization (MOPSO) and the Pareto-optimal solutions were successfully obtained. The trade-off was analyzed through multi-criteria decision making, and one Pareto-optimal solution was selected. The developed model and optimal points are useful for treatment of AR88 wastewater.

UV-light-responsive Ag/TiO2/PVA nanocomposite for photocatalytic degradation of Cr, Ni, Zn, and Cu heavy metal ions

The rapid growth of industrialization has led to the uncontrolled pollution of the environment, and rapid action is needed. This study synthesized Ag/TiO2/polyvinyl alcohol (PVA) nano photocatalyst for promising light-derived photocatalytic removal of heavy metal ions. The design of experiment (DOE) was used to study the effect of important factors (pH, reaction time, and photocatalyst dosage) to maximize the final performance of the photocatalyst. In the optimized condition, the Ag/TiO2/PVA nano-photocatalyst removed more than 94% of Cr6+ in 180 min, and the efficiency was more than 70% for Cu2+, Zn2+, and Ni2+ metal ions. The adsorption of the heavy metal ions on the photocatalyst was described well with the Langmuir isotherm, while the pseudo-second-order linear kinetic model fitted with the experimental data. The nano-photocatalyst's stability was confirmed after maintaining its performance for five successive runs. The enhanced photocatalytic activity for the heavy metal ions removal can be attributed to the presence of metallic silver nanoparticles (electron transfer and plasmonic fields mechanisms) and PVA, which delayed the recombination of electron–hole. The synthesized ternary Ag/TiO2/PVA nano-photocatalyst showed promising performance for the elimination of heavy metal ions and can be used for environmental remediation purposes.

Improved control-oriented polarization characteristic modeling for proton exchange membrane water electrolyzer with adaptive hunting game based metaheuristic optimization

The proton exchange membrane water electrolyzer has garnered increasing attention for the large-scale consumption of intermittent renewable energy to produce high purity “green hydrogen”, contributing to the realization of futural carbon neutrality goals. The necessity of control-oriented polarization characteristic modeling with accurate parameter identification remains a significant challenge for the performance evaluation of this type of water electrolyzer. In this paper, an improved control-oriented polarization characteristic modeling for proton exchange membrane water electrolyzer with adaptive hunting game based metaheuristic optimization is proposed to foster collaborative performance enhancement between model improvement and optimizer investigation. The inspiration behind model improvement lies in the nonlinear mechanism linkage between partial pressures of liquid water supplying and anode gas separator. Then, an adaptive hunting game based metaheuristic optimizer is proposed to address the inherent local multi-extremum distribution problem in the established model. Experimental data from a commercial water electrolyzer system is used to validate the effectiveness and practicability of the proposed model-optimizer pair. Detailed comparisons reveal that the improved model development, with its consideration of critical mechanisms, ensures enhanced metaheuristic efficiency in the designed optimizer, both in terms of robust intensity evaluation and efficient quasi-Newton based local search. Statistically, the collaboration performance enhancement reaches up to 7.33% when compared with the other model-optimizer.

Synthesis, characterization of KAlPO4F and its application for methyl violet adsorption

KAlPO4F was prepared hydrothermally at 453 K, a time-saving method using cheap reagents. The white solid was characterized by different methods such as powder X-ray diffraction, thermal analysis, SEM and UV–Vis diffuse reflectance. The compound was successfully tested for the removal of methyl violet (MV), a hazardous dye. Experiments were carried out as a function of contact time, initial concentration, temperature and pH. The amount of dye uptake was found to vary with increasing initial solution pH and maximum adsorption was observed at pH 10; the equilibrium was attained in 270 min. The amount of dye uptake (mg/g) was found to increase with increase in dye concentration and contact time. The pseudo-first-order, pseudo-second-order, Elovich and intraparticle diffusion models were applied to fit the experimental data to elucidate the kinetic adsorption. The pseudo-second-order model was the best to describe the adsorption process. Different models analyzed the equilibrium isotherms; the applicability for the experimental data follows the order: Freundlich > Langmuir > Temkin. The thermodynamic parameters: ΔH° (39.034 kJ mol−1), ΔS° (134 J K−1 mol−1) and ΔG° (− 367.01 J mol−1) indicated that the adsorption process is endothermic and spontaneous with increasing disorder at the solid–solution interface.

Sorption of picolinic acid by Cu(II)-containing sulfocationite KU-2-8

Objectives. To study the equilibrium distribution of components between KU-2-8 sulfocationite and an aqueous solution containing picolinic acid and Cu(II); to show the possibility of immobilization of cations of picolinic acid and Cu2+ in sulfonic cation exchanger KU-2-8; to calculate the component compositions of the equilibrium solution, in order to obtain the required ionic composition of the KU-2-8 sulfonic cation exchanger according to the selectivity coefficients of binary ion exchange, and the constants of formation of such complexes in water.Methods. The concentrations of the individual components in multicomponent solutions were calculated using the HySS 2009 program (Hyperquad Simulaton and Speciation). The calculation of the equilibrium ionic compositions of KU-2-8 sulfocationite was performed using the selectivity coefficients of binary ion exchanges and the formation constants of complexes of picolinic acid with Cu2+ and H+ cations. Experimental study of the equilibrium distribution of components between aqueous solutions of picolinic acid, copper nitrate, and KU-2-8 sulfocationite was carried out by means of the dynamic method at a temperature of 298 K. Fourier-transform infrared spectroscopy and electron paramagnetic resonance spectroscopy were used, in order to determine the ionic forms of the components contained in the sulfocationite.Results. It was shown that the equilibrium solution contains H+ protons, Cu2+ cations, LH picolinic acid molecules, protonated picolinic acid cations [H2L]+, deprotonated picolinic acid anions L-, Cu2+ complexes with the deprotonated picolinic acid anion [CuL]+, and Cu2+ complexes with two anions of deprotonated picolinic acid [CuL2]. The concentration of H+, Cu2+, and [H2L]+ cations in the solution significantly exceeds the concentration of other components at pH values from 0 to 0.5. The content of [CuL]+ cations and neutral complexes [CuL2] increases significantly in the solution, while the [H2L]+ cations disappear at pH greater than 1. It was experimentally established that the concentrations of picolinic acid and copper in the polymer phase are many times higher than the concentrations of these components in an aqueous solution. The partition coefficients are about 24 and 210 for picolinic acid and Cu(II), respectively. The calculated dependencies of the concentrations of Cu2+, [H2L]+, H+, [CuL]+ cations in the polymer vs pH of an equilibrium solution containing picolinic acid were obtained. The experimental data on the concentrations of all cations in the ion exchanger is in the intervals of the calculated compositions within the limits of measurement errors.Conclusions. KU-2-8 sulfocationite is proposed as a container for obtaining drugs based on picolinic acid and Cu2+ cations. It was shown that the selectivity coefficients of binary ion exchanges and the formation constants of [H2L]+, [CuL]+ complexes can be used to precalculate the ionic compositions of the equilibrium solution, in order to obtain the required compositions of the sulfocationite.

Interpreting correlations in stress‐dependent permeability, porosity, and compressibility of rocks: A viewpoint from finite strain theory

AbstractCharacteristics of stress‐dependent properties of rocks are commonly described by empirical laws. It is crucial to establish a universal law that connects rock properties with stress. The present study focuses on exploring the correlations among permeability, porosity, and compressibility observed in experiments. To achieve this, we propose a novel finite strain‐based dual‐component (FS‐DC) model, grounded in the finite strain theory within the framework of continuum mechanics. The FS‐DC model decomposes the original problem into the rock matrix and micro‐pores/cracks components. The deformation gradient tensor is utilized to derive the constitutive relations. One of the novelties is that the stress‐dependent variables are calculated in the current configuration, in contrast to the reference configuration used in small deformation theory. The model has only a few number of parameters, each with specific physical interpretations. It can be reduced to existing models with appropriate simplifications. Then, model performance is examined against experimental data, including permeability, porosity, compressibility, volumetric strain and specific storage. It proves that the variations of these properties are effectively described by the proposed model. Further analysis reveals the effect of pores/cracks parameters. The validity of the FS‐DC model is examined across a broad range of pressures. The results show that rock properties at high confining pressures (300 MPa) differ from those observed under relatively low pressures (200 MPa). This disparity can be attributed to inelastic behaviors of micro‐structure, wherein the rock skeleton undergoes permanent deformation and breakage.

Development of a soft sensor for fouling prediction in pipe fittings using the example of particulate deposition from suspension flow

Fouling is the unwanted accumulation of material on a processing surface which is an especially problematic issue in the food industry. Characterizing or predicting fouling through traditional methods or models is a challenge due to the complexity of fouling mechanisms. Machine learning (ML) techniques can overcome this challenge by creating models for prediction directly from experimental data. Unfortunately, the results can be hard to interpret depending on the algorithm.Here, a soft sensor is generated from an extensive data set to predict the fouling of a model particle material system. This is performed inside two different pipe fittings, an inaccessible and accessible fitting (e.g., for sensor measurements). Additionally, dimensional analysis (DA) is conducted to identify the correlations responsible for fouling while keeping descriptors with physical meaning. The resulting dimensionless numbers (DNs) are further processed by three ML algorithms: linear regression (LR), symbolic regression (SR), and random forest (RF).The soft sensor generated using a RF outperformed the other two regressors for the dimensional (Q2=0.90±0.08) and for the dimensionless data (Q2=0.88±0.09). The parameter time and particle mass fraction were determined to be most influential. Furthermore, seven DNs were obtained allowing a reduced experimental design.