Key Process Parameters Research Articles

Machine learning screening tools for the prediction of extraction yields of pharmaceutical compounds from wastewaters

Pharmaceutical compounds have become an increasingly important source of pollutants in wastewaters being conventional treatments ineffective in removing them, so they are commonly discharged into the environment. Pharmaceuticals can be successfully removed using liquid-liquid extraction, and COSMO-RS can be used to predict interactions and identify the most promising solvents. However, COSMOtherm models cannot account for key process parameters, which reduces the accuracy of these computational models. Therefore, there is a need for alternative computational approaches to accurately predict the extraction yields of pharmaceuticals which can incorporate both processing and interaction variables. This work used machine learning to predict the extraction yield of eleven pharmaceuticals using eight solvents. Six regression models and two classification models were explored. The best performance was obtained with ANN regressor (test MAE: 4.510, test R2: 0.884) and RF classifier (test accuracy: 0.938, test recall: 0.974). The RF regression analysis and classification also showed key extraction yield features: solvent-to-feed ratio, n–octanol–water partition coefficient, hydrogen bond and Van der Waals contributions to excess enthalpy, and pH distance to nearest pKa. Machine learning showed as an excellent tool for screening and selecting the most promising solvents and process conditions to remove pharmaceuticals from wastewater.

Biodiesel synthesis through Gossypium hirsutum oil transesterification using reusable nanocatalysts: An energy intensive approach optimization

The present study intends to synthesize biodiesel from Gossypium hirsutum (cottonseed) oil using reusable copper (Cu) nanoparticles and optimize the transesterification process parameters to achieve a higher yield of Gossypium hirsutum biodiesel (GHB). The Cu nanoparticle was synthesized through the polyol method, and its fundamental characteristics were analyzed using scanning electron microscopy, X-ray diffraction, and energy-dispersive X-ray spectroscopy. The cottonseed oil, obtained through mechanical expeller, was then subjected to a two-step transesterification process for the conversion into GHB. Key process parameters such as Cu catalyst concentration (CuC), methanol-oil-ratio (MOR), and reaction time (RT) were optimized at a reaction temperature of 60 °C. The GHB’s elemental composition, Fourier-transform infrared spectroscopy, and physicochemical properties were analyzed. The results indicate that the nanoparticle characterization provided valuable insights into its properties, showcasing its recyclability for five consecutive cycles. The optimized process parameters for achieving a higher GHB yield were determined as CuC of 211 ppm, MOR of 8.5, and RT of 104.5 minutes, resulting in GHB yield of 95.6%. Furthermore, an exploration of an energy-intensive approach revealed that a slight reduction in CuC by 1% and an increase in the MOR by 2.4% significantly reduced the RT by 36.3%, with only a marginal decrease in GHB yield (2.6%). Confirmation experiments validated the predicted values, and the fuel properties of GHB strongly support its efficacy as a valuable fuel source.

A Real-Time Monitoring Method for Selective Laser Melting of TA1 Materials Based on Radiation Detection of a Molten Pool.

Selective laser melting (SLM) technology is a promising additive manufacturing technology. However, due to the numerous influencing factors in this complex process, a reliable real-time method is needed to monitor the forming process of SLM. The molten pool is the smallest forming unit in the SLM process, the consistency of which can effectively reflect the quality of the printing process. By using a coaxial optical path structure and a compound amplifier circuit, high-speed acquisition of molten pool radiation can be realized. Next, single factor analysis and orthogonal experimentation were used to investigate the influence levels of key process parameters on the radiation of molten pool. In addition, numerical simulation was carried out with the same parameter setting schemes, the results of which are consistent with those in radiation detection experiments. It is shown that the laser power has the greatest effect on the radiation of the molten pool, while the scanning speed and the hatch spacing have little effect on the radiation. Finally, the positioning experiment involving the small hole structure was carried out, and the experimental results showed that the device could accurately locate the position coordinates of the given hole structure.

Enhancing municipal solid waste leachate treatment efficiency: AI-based prediction of electrocoagulation/flocculation recovery using iron electrodes

ABSTRACT This study addresses a gap in municipal leachate (MUPL) treatment by introducing a pioneering application of artificial intelligence (AI) in the electrocoagulation/electroflocculation (EC/EF) process utilizing iron electrodes. The overarching aim is to demonstrate the efficacy of AI, particularly a multi-layer perceptron (MLP)-based feed-forward artificial neural network (ANN) incorporating the Levenberg-Marquardt (LMb) algorithm, in predicting and optimizing EC/EF outcomes for turbidity (TDY) removal. The research methodology involved experimentation and robust ANN data modeling. The significance of this work emerges from the successful integration of AI, showcasing its potential in advancing wastewater, demonstrated through a strong positive correlation (0.994) between the ANN model predictions and experimental outcomes. The study achieves a remarkable 99.4% TDY removal at an electrolysis time of 10 min and contributes valuable insights into the critical parameters influencing the EC/EF process. Results from the ANN modeling exhibit high predictive accuracy, supported by elevated R-squared values and minimal mean square error. Statistical analyses underscore the significance of key process parameters, highlighting the influential roles of current intensity and settling time. The study emphasized the favourable impact of maintaining an acidic pH range, as it reduced electrostatic repulsion between particles, facilitating pollutant agglomeration, and identified electrolysis time as a key factor in enhancing treatment efficiency, supported by a strong positive correlation between electrolysis time and TDY reduction. Energy cost savings were realized by not requiring temperature elevation. Achieving a 99.4% TDY removal translates to substantial reductions in other pollutants present in the MUPL, thereby elevating water quality and ensuring compliance.

A Review on Process Parameter Optimization in Material Extrusion Additive Manufacturing using Thermoplastic

Material extrusion is one of the most commonly used additive manufacturing techniques which utilizes thermoplastics as the building material. The quality and performance of parts produced via material extrusion depends highly on the process parameters selected. This paper reviews the work done by various researchers on optimizing key process parameters like extrusion temperature, layer thickness, infill percentage, infill pattern etc. in material extrusion 3D printing using thermoplastics like PLA, ABS and nylon. The effects of these parameters on properties like surface roughness, dimensional accuracy, mechanical strength are discussed. Statistical tools like Taguchi method and response surface methodology that have been applied for parameter optimization are also reviewed. The review highlights the need for further process optimization studies considering part design, build orientation and post processing to unleash the full potential of material extrusion 3D printing..

Using Bayesian Regularized Artificial Neural Networks to Predict the Tensile Strength of Additively Manufactured Polylactic Acid Parts

Additive manufacturing has transformed the production process by enabling the construction of components in a layer-by-layer approach. This study integrates Artificial Neural Networks to explore the nuanced relationship between process parameters and mechanical performance in Fused Filament Fabrication. Using a fractional Taguchi design, seven key process parameters are systematically varied to provide a robust dataset for model training. The resulting model confirms its accuracy in predicting tensile strength. In particular, the mean squared error is 0.002, and the mean absolute error is 0.024. These results significantly advance the understanding of 3D manufactured parts, shedding light on the intricate dynamics between process nuances and mechanical outcomes. Furthermore, they underscore the transformative role of machine learning in precision-driven quality prediction and optimization in additive manufacturing.

Optimization of process parameters in plasma arc cutting of commercial-grade aluminium plate

Abstract Plasma arc cutting (PAC) has emerged as a versatile and efficient method for the precision cutting of various materials, including commercial-grade aluminium plates. The optimization of process parameters is crucial for achieving high-quality cuts, minimizing material wastage, and enhancing overall productivity. This study aims to systematically investigate and optimize the key process parameters in PAC of commercial-grade aluminium plates. The experimental design involves the manipulation of parameters such as arc current, gas pressure, and workpiece thickness. A Design of Experiments approach, specifically Taguchi’s orthogonal array, is employed to efficiently explore the parameter space and identify the optimal combination of settings. The response variables considered for optimization include minimum surface roughness, minimum burr height, and maximum material removal rate (MRR). Analysis of variance is performed to get the percentage influence of each process parameter on the performance characteristic. The results obtained from the optimization process are expected to provide valuable insights into enhancing the efficiency and precision of PAC for commercial-grade aluminium plates. Arc current is found to be the most significant parameter in altering the surface roughness. The thickness of the material is the most significant parameter in altering burr height. None of the parameters is found to be significant in altering the MRR from Analysis of Variance analysis. From signal-to-noise ratio analysis and average performance graph, the optimum combination of processes in altering the bur height and MRR are found as arc current at 50 amp, the gas pressure at 5.4 bar, and the thickness of the workpiece at 6 mm.

Process parameter optimization for laser powder directed energy deposition of Inconel 738LC

Processing nickel-based superalloys is one of the major challenges in the additive manufacturing (AM) field. Nickel-based superalloys consist of several alloying elements, which make them prone to the formation of undesired phases and cracking due to the complex thermodynamics during the metal AM processes. In order to produce defect-free parts from nickel-based superalloys via metal AM processes, optimization of process parameters is required. In this study, the effect of laser powder directed energy deposition (LP-DED) process parameters on the geometrical properties and porosity ratio of Inconel 738 LC (low carbon) is investigated. Geometrical specifications, including the track height, width, and wetting angle are the major factors that indicate the quality of the final product. The analysis of variance (ANOVA) and response surface methodology (RSM) were developed in order to predict the geometrical specifications of single tracks from the key process parameters, namely laser power, scan speed, and powder feed rate. An optimum set of process parameters was obtained through multi-objective optimization by minimizing the porosity ratio, crack formation and maximizing the deposition rate. The results were verified and a good agreement with the experimental measurements was achieved. The produced bulk sample showed less than one percent porosity content, which indicates the effectiveness of single-track optimization prior to bulk sample production. The overall microstructure of the bulk sample showed a high content of MC carbides resulting from the abundance of alloying elements.

Surface topography characterization of USMM during machining of zirconia ceramic using silicon carbide abrasives: An experimental and simulation approach

Zirconia is a highly biodegradable ceramic material with excellent fracture resistance, in biomedical engineering, particularly dental implants. This research work has been focused on optimizing the quality of micro-hole generation in zirconia ceramic through ultrasonic micromachining (USMM). Three key process parameters such as abrasive slurry concentration, tool feed rate, and power rating are considered in this research work. The material removal rate, overcut, and taper angle are considered as responses. Response surface methodology has been employed for modeling during the USMM process, and a mathematical model has been developed to understand material removal mechanisms. Finite element analysis has been utilized to provide insights into the impacting process for industry requirements. A 3D model has been created to perform dynamic analysis under practical conditions. Multi-objective optimization has been applied to achieve optimum material removal rate (MRR), overcut, and taper angle. From multi-objective optimization, a slurry concentration of 49.59% g/l, tool feed rate of 1.16 mm/min, and power rating of 386.87 W has been found and in this parameter settings maximum MRR of 0.5333 mm3/min, minimum taper angle of 0.3428 degrees, and minimum overcut of 36.64 µm has been obtained during machining of ZrO2 ceramics.

Insights to Study, Understand and Manage Extruded Dry Pet Food Palatability.

Pet food production is a fast-growing industry. While extruded dry pet food is the favored pet food due to its convenience of use, it may have poorer palatability than other pet foods such as wet pet foods. However, palatability plays a pivotal role in meeting nutritional requirements or providing therapeutic benefits in cats and dogs, as it ensures food acceptance. Thus, both academics and manufacturers conduct routine palatability tests to assess acceptance and preference of products among pets, alongside sensory analyses involving human panels. Palatability is greatly influenced by species-specific and environmental factors in cats and dogs. The review will hence present the current knowledge on palatability assessment and animal food perception; it will then aim to explore strategies for effectively managing palatability in dry pet food by examining the impact of key ingredients and process parameters on the finished product's palatability. Moreover, the demands and needs for sustainable and healthier products as well as supply constraints present novel challenges and opportunities for academics and manufacturers.

Microstructures and mechanical properties of copper-to-glass laser transmission welding employing a nanosecond pulsed laser

This study focuses on the interfacial characteristics of glass/copper welds achieved through laser transmission welding, utilizing a nanosecond pulsed laser. The investigation explores key processing parameters, specifically laser line energy intensity and total scan numbers, and their impacts on weld performance. Through meticulous analysis covering bond strength, cross-sectional surface morphologies, micro- and nano-structures, phase formations, and elemental compositions, the study systematically addresses the weld formation mechanism. The research elucidates potential reasons for diverse bonding characteristics, emphasizing the formation of Cux+-O-Si compounds (primarily Cu+-O-Si and Cu2+-O-Si) at the glass/copper interfacial region, as revealed by the XPS spectrum and HR-TEM images. These findings highlight redox reactions facilitated by strong intra-mixing through diffusion and convection during welding. The shear force separation test unveils an impressive maximum bond strength of 34.9 MPa, surpassing previously reported results, despite the indication of a brittle fracture in the separated surface morphologies. The correlation between a larger weld zone, abundant eutectic Cux+-O-Si compounds, and a uniformly re-solidified glass region is closely linked to the resulting high weld strength. This study underscores the direct relationship between laser irradiating energy and the extent of the weld zone, while the number of repeated scans proved to be an effective approach for regulating the elemental composition and microstructures in the weld. Overall, these findings contribute valuable insights into the weld formation mechanism and offer practical considerations for optimizing glass/copper welds through laser transmission welding.

Loading Determination of DMTr-substituted Resins for Large-scale Oligonucleotide Synthesis.

The loading (i.e., substitution) of solid supports for oligonucleotide synthesis is an important parameter in large-scale manufacturing of oligonucleotides. Several key process parameters are dependent on the substitution of the solid support, including the number of phosphoramidite nucleoside equivalents used in the coupling step. For dimethoxytrityl (DMTr)-loaded solid supports, the substitution of the resin is determined by quantitatively cleaving the DMTr protecting group from the resin under acidic conditions and then analyzing the DMTr cation extinction by UV/vis spectroscopy. The spectrometric measurement can be performed at 409 nm or the global extinction maximum of 510 nm. The substitution is then calculated based on the Lambert-Beer law analogously to the substitution determination of Fmoc-substituted resins. Below, the determination of the molar extinction coefficient at 510 nm in a solution of 10% dichloroacetic acid in toluene and subsequent determination of the DMTr loading of DMTr-substituted resins is reported. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Determination of the molar extinction coefficient at 510 nm in DCA Deblock solution Basic Protocol 2: Substitution determination of DMTr-substituted resins by cleavage of the DMTr cation.

Parametric influence and effect of cooling channel on bead geometry profiles of ER70S-6 manufactured using wire arc additive manufacturing

This study investigated the complex relationship between key process parameters and their effects on weld bead geometrical profile in wire arc additive manufacturing (WAAM) of ER70S-6 low-carbon steel for part production. Specifically, the influence of wire feed rate (WFR), traverse speed (TS), and voltage (U) on weld bead height (WBH), weld bead width (WBW), and weld bead penetration (WBP) was meticulously analyzed. Research methodology integrated the Taguchi technique, analysis of variance (ANOVA), regression modeling, and gray relational analysis (GRA) to investigate and optimize process parameters. Results revealed distinct patterns, with increasing WFR leading to proportional increases in WBH, while elevated TS or U results in reduced WBH due to their impact on arc behavior and heat input. Conversely, WFR and U exhibit notable effects on WBW and WBP, underscoring the importance of optimizing process parameters for desired weld bead geometries. To tackle multi-objective optimization, GRA efficiently determines optimal process parameters for WBH, WBW, and WBP: WFR at 6 m/min, TS at 4 mm/sec, and U at 24 V. Furthermore, the inclusion of a cooling channel beneath the substrate plate significantly influences weld bead geometry, enhancing WBH and WBW while impacting WBP. The cooling channel’s ability to dissipate heat promotes faster solidification, ultimately improving weld bead quality and integrity. These findings offer valuable comprehensions for improving WAAM processes and enhancing the efficiency and quality of part fabrication.

Investigation and Optimization on Parameters of Gas Additive Powder Mixed Near Dry –EDM (GAPMNDEDM) by using Taguchi based- Grey Relational Optimization

The Gas Additive Powder Mix near Dry Electric Discharge Machining (GAPMND-EDM) serves a manufacturing purpose, specifically for cutting hard materials. This involves the removal of material from a workpiece via fusion, ablation, and evaporation caused by the heat energy generated through electric sparks during energy supply. The process offers several advantages in terms of performance and characteristics. This research project aims to optimize key process parameters, including material removal rate, surface roughness, residual stresses, and microhardness. The study employs a methodology that combines the standard deviation-based objective weighting method with GRA (Gray Relational Analysis) optimization to enhance the hybrid GAPMND-EDM process when applied to EN-31 material. Experimental runs were conducted to evaluate the impact of various input factors, such as pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration. The taguchi-based GRA method was utilized for this purpose, and the experimental design followed an L-27 orthogonal array with the assistance of Minitab-19 software. A total of 27 experiments were performed, encompassing diverse combinations of process parameters. Subsequently, an ANOVA (Analysis of Variance) was executed to analyze the influence of pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration on Material Removal Rate (MRR), Surface Roughness (SR), Residual Stress (RS), and Microhardness (MH). The result shows the optimal combination of parameters, denoted as A2B2C2D1, was identified as the preferred configuration.

Ultrasonic fortification of interfiber autohesive contacts in meltblown nonwoven materials

Autohesion is a unique class of adhesion that enables the bonding of two identical surfaces by establishing intimate contact at interfaces. Creating intimacy between two identical surfaces poses a challenging task, often constrained by the presence of surface roughness and chemical heterogeneity. To surmount this challenge, we document a variety of autohesive traits in polypropylene-based meltblown nonwovens, accomplished through a facile, scalable, energy-efficient, and cost-effective ultrasonic bonding process. The mean work of autohesion for a single polypropylene bond, serving as a figure of merit, has been computed by extending the classical Johnson−Kendall−Roberts (JKR) theory by factoring in peel strength along with key fiber and structural parameters of nonwoven materials. Achieving a high figure of merit in ultrasonically bonded nonwovens hinges on the synergistic interplay of key process parameters, including static force, power, and welding speed, with the fiber and structural properties acting in concert. In this regard, peel-off force analysis has also been conducted on a series of twenty-seven ultrasonically bonded meltblown nonwovens prepared using a 33 full factorial design by systematically varying process parameters (static force, power, and welding speed) across three levels and extension rate. X-ray microcomputed tomography (microCT) analysis has been performed on select ultrasonically bonded nonwoven samples to discern their bulk characteristics. A broad spectrum of mean work of autohesion for a single polypropylene bond, ranging from 1.88 to 9.93 J/m², has been ascertained by modulating key process parameters.

Seasonal tourism's impact on wastewater composition: Evaluating the potential of alternating activated adsorption in primary treatment

Primary treatment processes have gained attention in recent research and development due to their potential for redirecting carbon towards anaerobic digestion, which can subsequently be used for the production of biomethane. The alternating activated adsorption (AAA) process is implemented on full-scale at several wastewater treatment plants across Europe. However, there is a lack of full-scale studies of advanced carbon capture technology implementations in literature. This study demonstrates the ability of a full-scale AAA process to remove and redirect carbon in a region heavily influenced by tourism. Periods in high and off-season were compared to study the impact of tourism on the composition of the wastewater and the AAA-process. The wastewater characteristics of the high season differed significantly from the low season. During the high season, the PE increased by 37 %, total suspended solids went up by 75 % and chemical oxygen demand increased by 58 %, compared to the low season. Additionally, 80 % of the low volatile lipophilic substances (LVLS) measured were attributed to the impact of tourism. A mass-balance of primary treatment for chemical oxygen demand (COD) and LVLS was conducted for both trial periods. The primary treatment was able to eliminate 56 % of the COD and 62 % of the LVLS in the non-tourist season and 53 % of the COD and 54 % of the LVLS in the tourist season. The increased wastewater load was effectively managed in the AAA-process. Key process parameters like sludge settling characteristics, hydraulic retention time and total suspended solids removal rates remained stable during the high season in winter.

Effective development of intensified perfusion culture based on shake tube semi-continuous bioreactors and optimal osmolarity constraint

The development of perfusion strategies supporting ultra-high cell growth and productivity for the intensified perfusion cultures (IPC) is very challenging. In this study, a novel approach was developed to address this issue by utilizing the shake tube semi-continuous bioreactors (ST) combining with a concentrated feed from the traditional fed-batch (TFB) platform. First, the impact of operating conditions in the ST were evaluated to identify those that best reflected the cell performance for the benchtop perfusion bioreactors (STR). Subsequently, the optimization of several key process parameters was performed in the ST along with the metabolic data obtained for the design of the IPC. Further, an empirical model to predict culture osmolarity was developed, verified, and subsequently utilized to determine the best combinations of the basal and feed perfusion rates ensuring the optimal osmolarity at the targeted viable cell density for the IPC process. Finally, the designed perfusion strategies were successfully validated in two IPC processes operated at 80 and 100×106 cells/m in the 3-L STR with an approximate 9-fold increase in cumulative titers compared to the TFB process. Overall, this study offers a novel and prospective method for the effective development of the IPC process to achieve significant process intensification.

Interdependencies in Electrode Manufacturing: A Comprehensive Study Based on Design Augmentation and Explainable Machine Learning

AbstractElectrode manufacturing, as the core of battery cell production, is a complex process chain with a large number of interrelated parameters. An in‐depth understanding of the processes, their relevant parameters, and the resulting effects on intermediate and final product properties can accelerate the transition toward quality‐oriented, efficient battery cell production. Given the complexity of the process chain, data‐driven models have emerged as promising solutions for analyzing the existing interdependencies. The accuracy and effectiveness of these models significantly depend on the quality and comprehensiveness of the underlying data. With a low‐quality dataset, there is an increased risk of drawing inaccurate conclusions or generating misleading results. This article aimed to demonstrate a use case for the evaluation and enhancement of historical datasets to provide a statistically robust foundation for the development of machine learning models. The study was based on pilot‐scale anode manufacturing and covered variations in the coating, drying, and calendering processes. The key intermediate product and process parameters were used to predict two primary target variables: adhesion strength and discharge capacity at different C‐rates. To gain a better understanding of the analyzed interdependencies, explainable machine learning methods were adopted.

Mechatronic Spatial Atomic Layer Deposition for Closed‐Loop and Customizable Process Control

AbstractA customized atmospheric‐pressure spatial atomic layer deposition (AP‐SALD) system is designed and implemented, which enables mechatronic control of key process parameters, including gap size and parallel alignment. A showerhead depositor delivers precursors to the substrate while linear actuators and capacitance probe sensors actively maintain gap size and parallel alignment through multiple‐axis tilt and closed‐loop feedback control. Digital control of geometric process variables with active monitoring is facilitated with a custom software control package and user interface. AP‐SALD of TiO2 is performed to validate self‐limiting deposition with the system. A novel multi‐axis printing methodology is introduced using x‐y position control to define a customized motion path, which enables an improvement in the thickness uniformity by reducing variations from 8% to 2%. In the future, this mechatronic system will enable experimental tuning of parameters that can inform multi‐physics modeling to gain a deeper understanding of AP‐SALD process tolerances, enabling new pathways for non‐traditional SALD processing that can push the technology towards large‐scale manufacturing.

Eucalypt Extracts Prepared by a No-Waste Method and Their 3D-Printed Dosage Forms Show Antimicrobial and Anti-Inflammatory Activity.

The pharmaceutical industry usually utilizes either hydrophobic or hydrophilic substances extracted from raw plant materials to prepare a final product. However, the waste products from the plant material still contain biologically active components with the opposite solubility. The aim of this study was to enhance the comprehensive usability of plant materials by developing a new no-waste extraction method for eucalypt leaves and by investigating the phytochemical and pharmacological properties of eucalypt extracts and their 3D-printed dosage forms. The present extraction method enabled us to prepare both hydrophobic soft extracts and hydrophilic (aqueous) dry extracts. We identified a total of 28 terpenes in the hydrophobic soft extract. In the hydrophilic dry extract, a total of 57 substances were identified, and 26 of them were successfully isolated. The eucalypt extracts studied showed significant antimicrobial activity against Staphylococcus aureus, Pseudomonas aeruginosa, Bacillus subtilis, Candida albicans, Corynebacterium diphtheriae gravis, and Corynebacterium diphtheriae mitis. The anti-inflammatory activity of the dry extract was studied using a formalin-induced-edema model in mice. The maximum anti-exudative effect of the dry extract was 61.5% at a dose of 20 mg/kg. Composite gels of polyethylene oxide (PEO) and eucalypt extract were developed, and the key process parameters for semi-solid extrusion (SSE) 3D printing of such gels were verified. The SSE 3D-printed preparations of novel synergistically acting eucalypt extracts could have uses in antimicrobial and anti-inflammatory medicinal applications.