Process Optimization Research Articles

Optimizing Recycling Processes for Mixed LFP/NMC Lithium-Ion Batteries: A Comparative Study of Acid-Excess and Acid-Deficient Leaching

This study explores the optimization of hydrometallurgical processes for recycling lithium-ion batteries (LIBs) containing a mixture of lithium iron phosphate (LFP) and nickel–manganese–cobalt (NMC) cathodes. Two approaches were investigated: acid-excess leaching and acid-deficient leaching with residue recirculation. A design of experiments (DoE) framework was applied to assess the impact of key parameters, including sulfuric acid and hydrogen peroxide concentrations, as well as solid-to-liquid (S/L) ratios, on the dissolution yields of target metals (Ni, Mn, Co, and Li). Acid-excess leaching achieved nearly complete dissolution of target metals but required additional purification steps to remove impurities. Acid-deficient leaching with a 60% recirculation of leaching residue improved dissolution yields by up to 12.5%, reduced reagent consumption, and minimized operational complexity. The study also evaluated separation strategies for manganese and cobalt through solvent extraction. Results indicate that while acid-excess leaching offers higher yields, acid-deficient leaching with residue recirculation is more cost-effective and environmentally friendly. These findings provide valuable insights for developing sustainable LIB recycling technologies.

In situ alloying of AlCuSi using dual-wire-directed energy deposition with plasma

Abstract The current research explores additive manufacturing of a multi-phase material using dual-wire plasma-directed energy deposition technology. With this approach, new materials can be designed and tested easily on the basis of commercially available consumables. In this work, AlSi5 and CuAl8 solid wire consumables are used to produce a specific AlCuSi alloy by controlling the welding parameters and the wire feed ratio. Initial experimentation results in an alloy with 85.7 at.% aluminum, 8.4 at.% copper, 2.7 at.% silicon, and 3.2 at.% magnesium, but with some instabilities during the process. The presence of magnesium in the chemical composition could be related to plasma interaction with the substrate during the welding process. After optimizing the process parameters, the chemical composition obtained is about 76.3 at.% aluminum, 19.9 at.% copper, and 3.8 at.% silicon. Using microstructural analysis via light and scanning electron microscopy, defects such as pores and inadequately melted Cu wire material are observed in all materials produced. Although the optimization of the melting process improved the microstructure, it also increased the copper content, which in turn exerts a significant influence on the mechanical properties. Mechanical testing indicates significant embrittlement. The results underscore that the microstructure is heavily influenced by the chemical composition. Microstructural changes caused by the higher copper content, i.e., in particular the increase of the volume fraction of brittle intermetallic phases such as θ-Al2Cu, result in severe embrittlement of the obtained materials, denoted by higher hardness and reduced toughness. We conclude that the use of dual-wire plasma additive manufacturing can develop new materials by in situ alloying.

Researching user experience with artificial intelligence application for customer care services on e-commerce platform

This study is conducted in the context of the booming development of e-commerce shopping in Vietnam. The rising demand for online shopping has resulted in increased pressure on workforce optimization and automation of customer service processes on e-commerce platforms. To meet the need for online customer care and response, significant resources in manpower, time, and cost are required. Therefore, to optimize costs, reduce response time, and enhance customer care, applying artificial intelligence to customer service has become a top choice. The purpose of this study is to explore the factors that directly affect the customer service experience using artificial intelligence on e-commerce platforms in Vietnam. This aims to help businesses understand customer feedback and psychology to develop strategies to improve and advance this artificial intelligence application model. This study provides an overview of the factors directly influencing the customer experience in AI-driven customer service on e-commerce platforms. Based on this, it offers managerial implications to enhance the quality of customer service for online businesses operating on e-commerce platforms in Vietnam.

Implementation and evaluation of a solution for automated documentation of the doctor-patient dialogue (ADAPI) in ophthalmology using the example of the IVI routine

The increasing bureaucratic burden in everyday clinical practice impairs doctor-patient communication (DPC). Effective use of digital technologies, such as automated semantic speech recognition (ASR) with automated extraction of diagnostically relevant information can provide asolution. The aim was to determine the extent to which ASR in conjunction with semantic information extraction for automated documentation of the doctor-patient dialogue (ADAPI) can be integrated into everyday clinical practice using the IVI routine as an example and whether patient care can be improved through process optimization. In aprospective monocentric study at the Sulzbach Eye Clinic, 50patients were included in the period from 2020 to 2022. As part of aproject funded by the "Zentrale Initiative des Mittelstandes" (ZIM), ademonstrator was developed with the consortium partners and integrated into the hospital information system (HIS). For qualitative and quantitative evaluation, asurvey of patients was carried out using aquestionnaire before and after implementation of the ADAPI module, supplemented by adetermination of acceptance and possible time saving for users. The ADAPI module was successfully integrated into the HIS. The documentation of the findings and connection of the subsequent processes could be automated. An improvement in the DPC was reported for 13/50patients (26%). The majority 35/50 (70%) did not notice any subjective change. The average duration of the conversation in the area of conventional documentation was 4.46 min (1-10 min) and 3.9 min with ADAPI (1-10 min). With respect to the quality of the conversation within the DPC, no relevant differences could be shown with both types of documentation; however, larger case numbers and other areas of application still need to be evaluated. In the long term, the use of automated documentation solutions will sustainably improve the efficiency, completeness and consistency of clinical documentation.

The Effect of Alkaline Pretreatment of Spent Malting Grains for Biogas Production, Mathematical Modeling and Process Optimization

Spent grains from brewing industries are considered a suitable source for biogas production due to their high accessibility and affordability. However, lignocellulosic material, due to its fiber content (cellulose, hemicellulose, and lignin), meets implementation issues in the anaerobic digestion process. To enhance the efficiency of anaerobic digestion of brewery spent grain, an alkaline treatment was performed using sodium hydroxide (NaOH). The parameters of alkaline treatment were temperature (T) and soda-spent grain ratio (S/D). The characterization of the raw spent grains indicates that the organic matter (OM), total organic carbon (TOC), nitrogen (N), protein (P), N/C ratio, and pH were 99%, 57.39%, 3%, 18.75%, 19.13%, and 5.6, respectively. The respective contents of hemicellulose, cellulose, and lignin were 33.3%, 30.4%, and 6.5%. The pre-treatment process yielded a 5% reduction in hemicellulose and a 43% reduction in cellulose while increasing the volume of biogas produced from 0 to 577 mL. After pre-treatment, two biodigesters produced volumes of 23.03 mL/per day (100 °C with a soda/spent grain ratio of 0.01%) and 22 mL/per day (28 °C with a soda/spent grain ratio of 0.05%). The mathematical model showed an interaction between temperature and S/D ratio. The predictive modeling analysis presented that the maximum volume of biogas can be obtained as 23.3 mL/per day after pre-treatment of spent grains at 100 °C with a NaOH/spent grain ratio of 0.01.

Prospects of cold plasma in enhancing food phenolics: analyzing nutritional potential and process optimization through RSM and AI techniques

Consumption of plant-based food is steadily increasing and follows an augmented trend owing to their nutritive, functional, and energy potential. Different bioactive fractions, such as phenols, flavanols, and so on, contribute highly to the nutritive profile of food and are known to have a sensitivity toward higher temperatures. This limits the applicability of traditional thermal treatments for plant products, paving the way for the advancement of innovative and non-thermal techniques such as pulsed electric field, microwave, ultrasound, cold plasma, and high-pressure processing. Among these techniques, cold plasma would be an operative choice in plant-based applications due to their higher efficacy, greenness, chemical exclusivity, and quality retention. The efficiency of the plasma process in ensuring the bioactive potential depends on several factors, such as feeding gas, input voltage, exposure time, pressure, and current flow. This review explains in detail the optimization of process parameters of the cold plasma technique, ensuring greater extractability or retention of total phenols and antioxidant potential. Response surface methodology (RSM) is one of the common techniques involved in the optimization of these course factors. It also covers the convention of artificial intelligence-based methods, such as artificial neural networks (ANN) and genetic algorithms (GA), in evaluating the data on process parameters. The review critically examines the strengths of each optimization tool in determining the optimal process parameters for maximizing phenol retention and antioxidant activity. The ascendancy of these techniques was mentioned in the studies regarding fruit, vegetables, and their products, and they can also be applied to other food products.

Process optimization and kinetics of biodiesel production from microalgal oil using potassium doped biochar heterogeneous catalyst

Biodiesel is considered to be an economical and eco-friendly substitute to fossil fuels. The present research was focused on the synthesis of potassium doped biochar catalyst from wood dust waste. The synthesized activated biochar catalyst was subjected to characterization using various techniques such as FT-IR, SEM-EDAX, XRD analysis which showed possible higher catalytic efficiency. The microalgae Chlorella vulgaris oil was used for the biodiesel production through transesterification reaction using the synthesized potassium doped biochar catalyst. The reaction parameters were optimized using statistical methods and the optimized conditions were found to be of 5.46% of catalyst dosage, 10.39:1 of methanol to algal oil ratio, 61.41 °C of temperature and 75.3 min of time with the highest biodiesel yield of 91.9%. The reaction kinetics was studied and it was found to follow the first-order kinetics with an activation energy of 12.18 KJ/mol. The catalyst reusability study exhibited higher catalytic performance until fourth cycle. Overall, the utilization of microalgae as a biofuel source and industrial waste as a catalyst contributes to sustainable biodiesel production and promotes a greener environment by reducing dependency on fossil fuels and minimizing industrial waste.

Computational Model of the Effective Thermal Conductivity of a Bundle of Round Steel Bars

During the heat treatment of round steel bars, a heated charge in the form of a cylindrically formed bundle is placed in a furnace. This type of charge is a porous granular medium in which a complex heat flow occurs during heating. The following heat transfer mechanisms occur simultaneously in this medium: conduction in bars, conduction within the gas, thermal radiation between the surfaces of the bars, and contact conduction across the joints between the adjacent bars. This complex heat transfer can be quantified in terms of effective thermal conductivity. This article presents the original model of the effective thermal conductivity of a bundle of round steel bars. This model is based on the thermo-electric analogy. Each heat transfer mechanism is assigned an appropriate thermal resistance. As a result of the calculations, the impact of the following parameters on the intensity of heat transfer in the bundle was examined: temperature, the thermal conductivity of the bars, the thermal conductivity of the gas, diameter, and emissivity of the bar, and bundle porosity. Calculations were performed for a temperature range of 25–800 °C, covering a wide spectrum of variables, including bar diameters, bundle porosity, and type of gas. The knowledge obtained thanks to the calculations performed will facilitate the optimization of heat treatment processes for the considered charge. The greatest scientific value of the presented research is the demonstration that, thanks to the developed computational model, it is possible to analyze a very complex heat transfer phenomenon using relatively simple mathematical relationships.

Oscillatory nature in melt-gas-powder interactions during laser powder bed fusion process revealed by CFD-DEM coupled modelling

ABSTRACT The laser powder bed fusion (LPBF) process encompasses interactions between different material states, including melt, gas, and powder. While observational techniques can capture specific states, they cannot be combined to produce a comprehensive monitoring how they are mutually interacted. Thus, an integrated modelling approach is a crucial solution for revealing their interactions. This study investigates melt-gas-powder dynamics under varying laser scan speeds through a coupled CFD-DEM multiphase model. Findings reveal that metal vapour consistently transitions from an initialisation phase to a processing phase, exhibiting consistent behaviour in the former but varied responses in the latter. A relationship is identified between scan speed and vapour flow angle (VFA), with higher speeds broadening the VFA. Significantly, three oscillatory behaviours are observed: first, the oscillation of locally intensified pressure (LIP) sites on keyhole walls, where vapour ejection modes shift and may become periodic at intermediate scan speeds; second, the oscillation of two induced argon vortex flows with opposite swirling directions; and third, a simultaneously induced varying drag force on powder spatter. These oscillations clarify various spatter mechanisms, offer insights for process optimisation, and suggest implications for process stability. The study also proposes future research directions to further elucidate these mechanisms.

Advanced Data Engineering and Machine Learning Approaches for Accurate Yield Prediction in Semiconductor Manufacturing Processes.

In semiconductor manufacturing, yield prediction is a critical area where advancements in data engineering and machine learning can significantly impact production efficiency and cost-effectiveness. This paper explores the integration of data engineering pipelines with machine learning models to improve yield prediction. Using a scalable and efficient framework, we demonstrate how leveraging Databricks for data processing and model training enhances the prediction accuracy and provides actionable insights for process optimization. Keywords: Data Engineering, Machine Learning, Semiconductor Manufacturing, Yield Prediction, Databricks

TIG welding process on IS 2062A steel of 5 mm plate, utilizing a 2.5 mm diameter tungsten solid wire as electrode along with direct current straight polarity and argon inert gas was employed for shielding purpose

The quality as well as efficient welding is crucial to fulfil the consumer satisfaction and getting returns from production, which is mostly depends on weld bead properties for any welding process, which is influence by the selected welding method and its process variables. So, the significant efforts are to choose the appropriate welding process in addition the process parameters as well as their level. In an atmosphere of welding different technique have been developed in last few decades, the demand of Gas Tungsten arc welding (GTAW) or Tungsten inert gas welding (TIG) has been increasing day by day owing to it has potential to fabricate multiple metallic materials (similar/dissimilar), quality joint, smooth modification for industrialization, higher efficiency, and low capital cost. However, published literature regarding research on TIG welding of IS 2062A steel is notably scarce. Detailed discussions on the optimization techniques exploring the impact of process parameters such as welding current, traversing speed, and gas flow rate on both weld quality and bead geometry in TIG welding of mild steel are conspicuously absent in prior works. So it is very crucial for quality joint to applied process optimization on TIG Welding parameters. The study focused on tungsten inert gas welding process on 5 mm thick plate of IS 2062A steel, utilizing a 2.5 mm diameter tungsten solid wire as electrode along with direct current straight polarity and argon inert gas was employed for shielding purpose. To streamline experimentation, a multi-response optimization technique was employed, using Taguchi's L16 experimental design method. Among several welding parameters, the significant parameters like current (I), traversing speed (S), and gas flow rate (Gf) were considered, others parameters were kept constant. The study measured various aspects of bead properties, including penetration, bead width, and depth of HAZ, along with HAZ hardness. The optimized parameter settings, derived from the signal-to-noise (S/N) ratio, has been validated as improved the weldment properties along with possess highest grey relation grade, which highlighting the effectiveness of the Grey-based Taguchi method in enhancing joints properties as product quality together with efficiency in manufacturing. Quantitative assessment via the analysis of variance method was conducted to determine the relative significance of each variable on the overall output characteristics of the weldment.

Investigation of Digital Light Processing-Based 3D Printing for Optimized Tooling in Automotive and Electronics Sheet Metal Forming

This study addresses the emerging need for efficient and cost-effective solutions in low-volume production by exploring the mechanical performance and industrial feasibility of cutting tools that are fabricated using stereolithography apparatus (SLA) technology. SLA’s high-resolution capabilities make it suitable for creating precise cutting dies, which were tested on aluminum sheets (Al99.5, 0.3 mm, and AlMg3, 1.0 mm) under a 60-ton hydraulic press. Measurements using digital image correlation (DIC) revealed minimal wear and deformation, with tolerances consistently within IT 0.1 mm. The results demonstrated that SLA-printed tools perform comparably to conventional metal tools in cutting and bending operations, achieving similar surface quality and edge precision while significantly reducing the production time and cost. Despite some limitations in wear resistance, the findings highlight SLA technology’s potential for rapid prototyping and short-run manufacturing in the automotive and electronics sectors. This research fills a critical gap in understanding SLA-based tooling applications, offering insights into process optimization to enhance tool durability and broaden material compatibility. These advancements position SLA technology as a transformative tool-making technology for flexible manufacturing.

Parameter optimization of the chloride mediator-based electrochemical advanced oxidation process for the treatment of commercial azo dyes and actual dyeing effluent

This work focused on the development of an electrochemical system for direct and indirect electrochemical advanced oxidation process (EAOP) with a chloride mediator by electrogenerated active species for the degradation of unused toxic azo dye molecules of textile wastewater and the connection between cathodic and anodic processes. The experiments were carried out in a batch electrochemical cell for the degradation of Direct Red 1 (DR 1) and Reactive Orange 14 (RO 14) dyes. Eight different combinations of iron (Fe), stainless steel (Ss), copper (Cu), and graphite (Gr) electrodes were utilized as the cathode and anode. The influences of effluent initial concentration, pH, supporting electrolyte concentration, temperature, inter-electrode distance, current density, agitation rate, retention time, and radical scavengers on dye degradation have been critically examined. The optimum conditions were obtained at the monopolar-parallel (MP-P) connected electrodes (Gr- Ss) with 7.0 pH, 7.25 V voltage, 0.4 A current, 353 A/m2 current density, 1 g/L NaCl, and a distance of 3 cm between the electrodes. Under these conditions, the removal efficiency reached 95.6% within 60 min of operation. Additionally, a multiple regression analysis was performed to assess how operating conditions affected the effectiveness of dye removal. The actual textile wastewater was examined as well, and the results revealed significant decolorization. Finally, due to its great capacity, the EAOP system is a promising large-scale technique for treating dye-contaminated effluents.Graphical

Evaluation of pig farming residue as substrate for biomethane production via anaerobic digestion

Abstract Livestock farming and manure management contribute substantially to greenhouse gas (GHG) emissions in agriculture. Anaerobic digestion (AD) of manure is a promising strategy for mitigating these emissions. This study aimed to assess the biomethane potential (BMP) of various types of pig slurry, investigate factors that influence biomethane production, analyze degradation kinetics, and propose AD process optimization approaches. Thus, substrate analysis, BMP tests in batch assays, kinetic modeling, and principal component analysis (PCA) were conducted. In order to further quantify the effects of different substrate qualities in full-scale operation, biomethane production was simulated under steady-state conditions. Results indicated that piglet slurry had the highest volatile solids (VS)–specific BMP (203 ± 72 L kg−1 VS), followed by mixed slurry (202 ± 132 L kg−1 VS), fattening pig slurry (117 ± 56 L kg−1 VS), and sow slurry (86 ± 17 L kg−1 VS). The PCA revealed different substrate types and significant roles for VS, crude fat, volatile fatty acids concentration, and the carbon/nitrogen ratio in achieving high BMPs. First-order two-step kinetic modeling identified hydrolysis as the rate-limiting step, showing a determinant of rate-limiting step of < 0 for each sample. The simulation of continuous operation revealed notable differences in daily biomethane production (36.7–42.7 L day−1) between the different slurries at the same hydraulic retention time and BMP. This research underscores the variability in pig slurry characteristics, exemplified by a total solids range of 1.4–12.1%, and provides crucial insights for optimizing AD processes in livestock waste management.

Secondary ion mass spectrometry relative sensitivity factor of 69Ga in ZnS

Multispectral zinc sulfide (MS-ZnS) is an important broadband optical material used in various imaging and sensing technologies. To address the challenge of withstanding aerodynamic loads during operational use, surface hardening treatments of MS-ZnS using Ga2S3 have demonstrated effectiveness while preserving optimal optical properties. Secondary ion mass spectrometry (SIMS) is particularly well-suited for analyzing Ga incorporation in the subsurface of post-treated ZnS windows. Herein, we report the determination of the relative sensitivity factor (RSF) for Ga in a ZnS matrix to calibrate Ga concentration profiles. The RSFs of 69Ga in ZnS were obtained from multiple SIMS measurements on three 69Ga-implanted ZnS coupons at doses of 0.5 × 1015, 2.5 × 1015, and 5.1 × 1015 (at. cm−2). We found that the elemental RSF values are 1.78 × 1020 (±17%), 1.76 × 1020 (±21%), and 1.48 × 1020 (±13%) (at. cm−3), indicating a proportional relationship with the amount of Ga as deduced from standard analysis versus the ratio of 69Ga and 64Zn count rates. These RSF determinations provide a reliable tool for analytical and process optimization purposes.

A Sustainability-Oriented Digital Twin of the Diamond Pilot Plant

The pharmaceutical industry is undergoing a significant transition from batch to continuous manufacturing, driven by increasing regulatory requirements and sustainability pressures. Digital twins (DTs) play a pivotal role in facilitating this transition by enabling real-time data visualisation, process optimisation, and predictive analytics. While substantial progress has been made in the development and application of DTs, particularly in industries such as energy and automotive, there remains a critical need for further research and development focused on creating sustainability-oriented digital twins tailored to pharmaceutical processes. In the pharmaceutical sector, DTs are being progressively utilised not only for real-time monitoring and analysis but also as dynamic training platforms for engineers and operators, enhancing both operational efficiency and workforce competency. This paper examines the University of Sheffield’s Diamond Pilot Plant (DiPP), a facility showcasing the future of pharmaceutical manufacturing through the integration of Industry 4.0 technologies and advanced sensors. This paper focuses on developing a data-driven model to predict energy consumption in a twin-screw granulator (TSG) within the DiPP. The model, based on second-degree polynomial regression, demonstrates strong predictive accuracy with R-squared values exceeding 0.8. By optimising energy performance indicators, this work aims to improve the sustainability of pharmaceutical manufacturing processes. This research contributes to the field of pharmaceutical manufacturing by providing a foundation for creating energy models and advancing the development of comprehensive DT.

A Method for Reducing Offset in CMOS Operational Amplifiers

In the development of integrated circuits, operational amplifiers are indispensable basic units CMOS operational amplifiers are the core components in analog integrated circuits, which are widely used in signal acquisition, data processing, communications and other fields. components in analog integrated circuits, which are widely used in signal acquisition, data processing, communications and other fields, and their performance has a direct impact on the accuracy and stability of the entire system. With the continuous development of electronic technology, CMOS operational amplifiers play a vital role. operational amplifiers play a vital role in many fields. However, the misalignment problem has been one of the key factors affecting the performance of However, the misalignment problem has been one of the key factors affecting the performance of CMOS operational amplifiers. This paper introduces the basic principles of CMOS operational amplifiers, thoroughly studies the phenomenon of CMOS operational amplifiers' misalignment. This paper introduces the basic principles of CMOS operational amplifiers, thoroughly studies the phenomenon of CMOS operational amplifiers' misalignment, the definition and sources of the misalignment voltage and its effects, and analyzes the main reasons for its generation, including device mismatch, process deviation, and so on. In this paper, we summarize several methods to reduce distortion from dynamic distortion elimination technology and design and process optimization, which can effectively reduce the distortion voltage of CMOS op amps. The main dynamic misalignment elimination techniques are chopping and auto-zeroing, as well as a special form of auto-zeroing, i.e., correlation dual- sampling technique. Design and process optimization includes two methods of device selection and matching optimization and circuit topology design.

Industrial Automation using Artificial Intelligence

The pharmaceutical and consumer healthcare industries have been greatly impacted by artificial intelligence and machine learning. A subfield of computer science called artificial intelligence is able to analyze intricate medical data. The goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem solving, and decision making. AI is becoming more and more significant in many areas of pharmacy, including drug discovery and formulation of drug delivery, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies growth. This review focuses on the significant applications of AI in various pharmaceutical domains, including drug development and discovery, Many studies are being conducted to enhance the AI technology that is currently available in order to increase the efficiency of the pharmacy profession. Artificial intelligence and system mastery have seen a significant upsurge in recent years. It has lessened the effort required of humans to advance in their extraordinary lives. Numerous drug discovery implementations have been examined, demonstrating the technology's effectiveness in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR). Additionally, they are employed in clinical trials to generate and interpret data gathered from patient information. The pharmaceutical industry is currently having trouble maintaining its drug development programs due to rising R&D expenses and declining productivity. In addition to helping with experimental design, machine learning algorithms can forecast the toxicity and pharmacokinetics of potential drugs. This capability lessens the need for extensive and expensive animal testing by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that examine actual patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes

Development Validation of UV Spectrophoto Meter Method for Determination of Polyherbal Powder

The pharmaceutical and consumer healthcare industries have been greatly impacted by artificial intelligence and machine learning. A subfield of computer science called artificial intelligence is able to analyze intricate medical data. The goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem solving, and decision making. AI is becoming more and more significant in many areas of pharmacy, including drug discovery and formulation of drug delivery, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies growth. This review focuses on the significant applications of AI in various pharmaceutical domains, including drug development and discovery, Many studies are being conducted to enhance the AI technology that is currently available in order to increase the efficiency of the pharmacy profession. Artificial intelligence and system mastery have seen a significant upsurge in recent years. It has lessened the effort required of humans to advance in their extraordinary lives. Numerous drug discovery implementations have been examined, demonstrating the technology's effectiveness in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR). Additionally, they are employed in clinical trials to generate and interpret data gathered from patient information. The pharmaceutical industry is currently having trouble maintaining its drug development programs due to rising R&D expenses and declining productivity. In addition to helping with experimental design, machine learning algorithms can forecast the toxicity and pharmacokinetics of potential drugs. This capability lessens the need for extensive and expensive animal testing by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that examine actual patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes

Development and Optimization of a Real-Time Monitoring System of Small-Scale Multi-Purpose Juice Extractor

According to the concept of smart postharvest management, an information and communication technology sensor–based monitoring system is required in the juicing process to reduce losses and improve process efficiency. Such technologies are considered economically burdensome and technically challenging for small-scale enterprises to adopt. From this perspective, this study aimed to develop a smart monitoring system for the juicing processes in small-scale enterprises and to identify the optimal operating conditions based on the monitoring data. The system developed is equipped with two weight sensors attached to the twin-screw juice extractor, allowing for the automatic measurement of the weight of the raw material and the resulting juice product. The measured data are automatically transmitted and stored on a computer. Additionally, the system was designed to remotely control the speeds of the juicing and feeding screws, which are the primary controlling factors of the twin-screw juicer. Juice yield and processing time were optimized using carrots and pears. The optimal juicing and feeding speeds for pear yield were found to be 167.4 rpm and 1557 rpm, respectively; carrots achieved an optimal yield at a juicing speed of 502.2 rpm and feeding speed of 1211 rpm. In contrast, the processing time was minimized at juicing–feeding speeds of 6–6 and 7–5 for pears and carrots, respectively. Consequently, it was challenging to determine the optimal conditions for simultaneously optimizing the yield and processing time. This also suggests that the juicing process is affected by the properties of the fruits and vegetables being processed. By developing a system capable of accumulating the data necessary for the digitization of postharvest management and food processing, this research offers a valuable platform for the smart monitoring and optimization of the juicing process.