Key Process Parameters Research Articles

Experimental investigation and optimization of process parameters on tensile strength of carbon coated PLA material

Abstract This study explores the effect of key process parameters on the tensile strength of carbon-coated PLA (Polylactic Acid) material using additive manufacturing. The problem addressed is the need to enhance the mechanical properties of PLA, a widely used biodegradable polymer, by optimizing process parameters to improve its tensile strength when coated with carbon. The parameters investigated include infill pattern, printing speed, layer thickness, and orientation. A response surface methodology (RSM) was employed to design the experiments, while Minitab 15 software was used to analyze the data through analysis of variance (ANOVA). The results indicate that the optimal process parameters, linear infill pattern, printing speed of 20 mm/s, layer thickness of 150 µm, and Y-90 orientation, yield an average tensile strength of 56.4 MPa. A regression model was developed to predict tensile strength, with a high degree of accuracy (R² = 0.97). Confirmation tests verified the model’s predictions, contributing to the development of high-performance PLA-based materials for demanding applications.

Optimizing laser-induced phase transformations in Sb2S3 thin films: Simulation framework and experiments

We present a novel simulation approach combined with pulsed laser experiments, spectroscopic ellipsometry, and Raman spectroscopy to comprehensively analyze phase transformation dynamics in thin films. The simulations apply to any thin film stack and incorporate critical factors, such as thin film interference, heat transfer, and temperature-dependent optical properties during heating and melting. As a case study, we investigate the picosecond laser-induced amorphization of antimony sulfide (Sb2S3) thin films, a promising alternative to traditional phase-change materials in photonic applications to validate the simulation model. The computational efficiency of our simulations enables not only the investigation of the laser-induced phase transformation but also the optimization of key process parameters and parameter fitting. The simulations identified optimal film thickness and laser fluence parameters that maximize energy efficiency, melting effectiveness, and quenching rate while ensuring high reflectivity contrast between the amorphous and crystalline states. By constructing a wide-ranging, high-resolution parameter map of the laser fluence and film thickness dependence of the melting process, we demonstrate how this model guides the understanding of phase transformation dynamics. Raman spectroscopy confirms the polycrystalline to amorphous transition of Sb2S3 and provides a semiquantitative estimate of the amorphous fraction as a function of laser fluence, which is qualitatively consistent with the simulation predictions of the model. The open-source simulation framework, experimentally validated, provides valuable insights into laser-induced amorphization dynamics in Sb2S3 and related phase-change material thin films, enabling rapid optimization of photonic devices.

DEVELOPMENT OF AUTOMATED CONTROL SYSTEM AND REGISTRATION OF METAL IN CONTINUOUS CASTING

Context. Modern industrial enterprises face challenges that require introduction of latest technologies to improve efficiency and competitiveness. In metallurgy, one of key stages is continuous casting, where quality of products and economic performance of enterprise depend on accuracy and efficiency of process control. Products made using continuous casting technology are widely used in various industries due to their high mechanical properties, structural uniformity and cost-effectiveness. The development of automated metal management and registration system is becoming not only relevant, but also necessary to ensure stable and efficient production. The problem of improving quality of metal products has always been one of most important tasks in steel industry. Imperfect technological processes, human error and equipment malfunctions can lead to defects in finished metal products. This, in turn, affects final characteristics of products, their durability and reliability. To date, available sources have not yet found complete solution to this problem. Therefore, it is necessary to formulate problem and develop algorithm for operation of automated system for controlling and registering metal in continuous casting. Objective. The goal of work is to develop automated metal management and registration system to improve quality of metal products. Method. To achieve this goal, parametric model was proposed, which is formalized on basis of set theory. The model takes into account key parameters of continuous casting process: material characteristics, structural features of crystallizer, casting modes, metal level in crystallizer, and position of shot stopper. Results. The problem was formulated and key parameters were determined, which are taken into account in system’s algorithm, which made it possible to develop system for controlling parameters of continuous casting to solve problem of improving quality of metal products. Conclusions. To improve quality of metal products and stability of casting process, parametric model was created that is comprehensive, allows optimization of key parameters and ensures accuracy of process control by integrating not only modes of product formation, but also takes into account specific properties of source material (chemical composition of material grade, etc.) and design features of casting plant. Algorithm for automated control system has been developed that takes into account relationships between certain key parameters and ensures optimal control of casting process. Based on proposed complex parametric model and algorithm, automated control and metal registration system was created. The focus of work is on quality and efficiency of metal management and registration in continuous casting, based on modern methods of computer science and engineering. A comprehensive experimental comparison of developed system with commercial analogs in real production conditions was carried out, which allowed us to objectively assess its efficiency and reliability.

Interaction between material and process parameters during 3D concrete extrusion process

Extrusion of cement-based materials in additive manufacturing is influenced by a complex interaction of various material and process parameters. This study aims to investigate the impact of rheological properties and printing parameters on the extrudability of cement-based materials as well as the interaction between rheological properties of print materials and extrusion speed, as a key printing process parameter. 20 different print materials with diverse rheological properties were extruded at 10 different extrusion speeds. The effect of material parameters (i.e. rheological properties) and process parameters (i.e., extrusion speed) and their interaction during the extrusion process were evaluated using filament continuity, conformity and consistency. At a constant printing speed, a higher yield stress adversely impacted filament continuity in mixtures with a yield stress of over 260 Pa, whereas viscosity showed minimal influence on filament quality within the tested range of the rheological properties. The results of this study highlighted a significant correlation between material and process parameters and that adapting process parameters to variations in material properties is crucial for meeting printing criteria. The filament consistency of the mixtures with high yield stress was more sensitive to change in the extrusion speed than that of mixtures with low yield stress. An opposite trend, however, was observed for filament conformity where low yield stress mixtures showed a higher sensitivity to the extrusion speed. A procedure was suggested and applied to determine the optimum extrusion speed. Optimal extrusion speeds enabled printing mixtures with a broader spectrum of rheological properties with an acceptable visual quality, filament conformity and consistency requirements. With extrusion speed adjustment, the extrudability window was expanded from dynamic yield stress of 108–126 Pa to 108–263 Pa and plastic viscosity of 8.2–12.3 Pa.s to 5.1–16.4 Pa.s.

Investigation of mechanical properties in FFF- produced PLA samples using the Erichsen test: application of definitive screening and RSM

Purpose This study aims to investigate how printing parameters affect the mechanical properties of specimens produced through fused filament fabrication, using the Erichsen test to assess deformation characteristics and material durability under stress. Design/methodology/approach Polylactic acid (PLA) specimens were printed and tested in accordance with the ISO 20482 standard. Definitive screening was conducted to identify the most influential process parameters. This study examined the effects of four key process parameters – number of layers, layer height, crossing angle and nozzle diameter – on force, distension, peak energy and energy to break. Each parameter was assessed at three levels and a large number of required experiments was managed by using response surface methodology (RSM). Findings This study revealed that the number of layers, layer height and crossing angle are the most significant factors that influence the mechanical properties of 3D-printed materials. The number of layers had the greatest impact on the peak force, contributing 44.25%, with thicker layers typically enhancing material strength. The layer height has a significant effect on energy absorption and deformation, with greater layer heights generally improving these properties. Nozzle diameter contributed only 1.10%, making it the least influential factor; however, its impact became more pronounced in interactions with other parameters. Originality/value This paper presents a comprehensive experimental investigation into the effects of process parameters on the crack strength and behavior of 3D-printed PLA specimens using the RSM method. The documented results can be used to develop optimization models aimed at achieving desired mechanical properties with reduced variation and uncertainty in the final product.

Updated Review of Metal Nanoparticles Fabricated by Green Chemistry Using Natural Extracts: Biosynthesis, Mechanisms, and Applications

Metallic nanoparticles have found wide applications due to their unique physical and chemical properties. Green biosynthesis using plants, microbes, and plant/microbial extracts provides an environmentally friendly approach for nanoparticle synthesis. This review discusses the mechanisms and factors governing the biosynthesis of metallic nanoparticles such as silver, gold, and zinc using various plant extracts and microorganisms, including bacteria, fungi, and algae. The phytochemicals and biomolecules responsible for reducing metal ions and stabilizing nanoparticles are discussed. Key process parameters like pH, temperature, and precursor concentration affecting particle size are highlighted. Characterization techniques for confirming the formation and properties of nanoparticles are also mentioned. Applications of biosynthesized nanoparticles in areas such as antibacterial delivery, cancer therapy, biosensors, and environmental remediation are reviewed. Challenges in scaling up production and regulating nanoparticle properties are addressed. Power Point 365 was used for creating graphics. Overall, green biosynthesis is an emerging field with opportunities for developing eco-friendly nanomanufacturing platforms using abundant natural resources. Further work on optimizing conditions, standardizing protocols, and exploring new biosources is needed to realize the full potential of this approach.

Modelling process monitoring data in laser powder bed fusion: A pragmatic route to additive manufacturing quality assurance

Quality assurance remains a significant challenge for laser powder bed fusion and metal additive manufacturing. Despite system manufacturers offering process monitoring as a possible solution, datasets are large and cumbersome with practical use limited without direct comparative data. Model datasets would enable individual build validation, highlight deviations, and facilitate intelligent build planning whereby challenging features or build strategies could be pre-emptively assessed.Herein a pragmatic approach has been developed to model process monitoring data from a commercial system using a relatively simple algorithm. Using a heuristic method, the algorithm response has been fitted to an experimental dataset to derive governing constants and their relationship to key process parameters. Predictability of constants and model fit has been shown to improve with increasing line energy up to a maximum R2=0.8. Algorithm variable trends, supported by corresponding sensitivity analysis, identified two different behavioural regimes. Under low linear energy density (<0.2J/mm) the cumulative spacetime proximity time-weight variable shows a low sensitivity index, characterised by a flat model response reflected in the experimental data. At higher energies (≥0.2J/mm) algorithm variables become more predictable, reflected in stabilising sensitivity indices, as measurements adopt a form characteristic of the cumulative spacetime proximity function.Effectiveness has been demonstrated through presentation of experimental and model data. Refining the methodology to accommodate noise, geometry, and systematic behaviours are identified as key steps to future development. This feasibility study has laid the groundwork for a generalised predictive tool, capable of realising the quality assurance ambitions promised by laser powder-bed fusion process monitoring.

Scalable fabrication of vertically arranged Bi2Se3 crossbar arrays for memristive device applications

Despite the unique advantages of the memristive switching devices based on two-dimensional (2D) transition metal dichalcogenides, scalable growth technologies of such 2D materials and wafer-level fabrication remain challenging. In this work, we present the gold-assisted large-area physical vapor deposition (PVD) growth of Bi2Se3 features for the scalable fabrication of 2D-material-based crossbar arrays of memristor devices. This work indicates that gold layers, prepatterned by photolithography processes, can catalyze PVD growth of few-layer Bi2Se3 with 100-folds larger crystal grain size in comparison with that grown on bare Si/SiO2 substrates. We also present a fluid-guided growth strategy to improve growth selectivity of Bi2Se3 on Au layers. Through the experimental and computational analyses, we identify two key processing parameters, i.e., the distance between Bi2Se3 powder and the target substrate and the distance between the leading edges of the substrate and the substrate holder with a hollow interior, which plays a critical role in realizing large-scale growth. By optimizing these growth parameters, we have successfully demonstrated cm-scale highly-selective Bi2Se3 growth on crossbar-arrayed structures with an in-lab yield of 86%. The whole process is etch- and plasma-free, substantially minimizing the damage to the crystal structure and also preventing the formation of rough 2D-material surfaces. Furthermore, we also preliminarily demonstrated memristive devices, which exhibit reproducible resistance switching characteristics (over 50 cycles) and a retention time of up to 106 s. This work provides a useful guideline for the scalable fabrication of vertically arranged crossbar arrays of 2D-material-based memristive devices, which is critical to the implementation of such devices for practical neuromorphic applications.

Effect of Preheating Parameters on Extrusion Welding of High-Density Polyethylene Materials

High-density polyethylene (HDPE) has emerged as a promising alternative to fiber-reinforced plastic (FRP) for small vessel manufacturing due to its durability, chemical resistance, lightweight properties, and recyclability. However, while thermoplastic polymers like HDPE have been extensively used in gas and water pipelines, their application in large, complex marine structures remains underexplored, particularly in terms of joining methods. Existing techniques, such as ultrasonic welding, laser welding, and friction stir welding, are unsuitable for large-scale HDPE components, where extrusion welding is more viable. This study focuses on evaluating the impact of key process parameters, such as the preheating temperature, hot air movement speed, and nozzle distance, on the welding performance of HDPE. By analyzing the influence of these variables on heat distribution during the extrusion welding process, we aim to conduct basic research to derive optimal conditions for achieving strong and reliable joints. The results highlight the critical importance of a uniform temperature distribution in preventing defects such as excessive melting or thermal degradation, which could compromise weld integrity. This research provides valuable insights into improving HDPE joining techniques, contributing to its broader adoption in the marine and manufacturing industries.

Optimizing kerf quality in high-speed WEDM of thin woven CFRP composites: a taguchi, WASPAS, and PSO approach

The process of machining CFRP composites presents unique challenges, particularly in the context of WEDM. The inherent properties of CFRP composites, such as their low electrical conductivity, anisotropic nature, and heterogeneous composition, require further research to enhance their machinability through WEDM techniques. This study examines the enhancement of kerf characteristics such as kerf width (Wk), delamination factor (DFK), and cutting speed (CSK) in thin woven 0°/90° CFRP composites using high-speed WEDM. A Taguchi L16 experimental analysis was employed to analyze the impact of key process parameters, including pulse-on (Pon), pulse-off (Poff), and input current (I), in conjunction with CFRP parameters such as the CFRP thickness (T) and cutting direction on the kerf characteristics. The CFRP thickness ranged from 0.5 to 2.0 mm, and the cutting directions studied were horizontal and inclined 30° cuts. A multiple-response optimization strategy using the CRITIC-WASPAS approach coupled with a particle swarm optimization (PSO) algorithm were applied to identify the ideal process combination for various CFRP thicknesses. The findings indicated that the CFRP thickness, pulse-off time, and input current are the most statistically significant factors influencing the overall kerf characteristics. The cutting direction has a negligible effect on the kerf width but has conflicting effects on the delamination factor and cutting speed. Specifically, a horizontal cut decreases delamination, whereas an inclined 30° cut is preferable for achieving higher cutting speeds. For precise kerf cutting, optimal process combinations were determined: Pon (30 µs), Poff (30 µs), and I (ranging from 4 to 5 A) for 0.5 mm CFRP thickness, and Pon (30 µs), Poff (15 µs), and varying input currents of 4 A, ranging from 4 to 3 A, and 3 A for CFRP thicknesses of 1.0, 1.5, and 2.0 mm, respectively.

Study of Parameters Influencing Wrinkles in the Deep Drawing of Fiber-Based Materials Using Automatic Image Detection

The evaluation of wrinkles in deep-drawn fiber-based materials is crucial for the assessment of product quality and the optimization of manufacturing processes. Wrinkling is a common phenomenon in the deep-drawing process and is caused by tangential compressive stresses on the flanges of the blank. This phenomenon is particularly prevalent for fiber-based materials with high tensile depths and can seriously affect the appearance and mechanical properties of the final product. The objective of this study is to identify the key process parameters affecting wrinkling and to deepen the understanding of their roles and interactions using wrinkle data for deep-drawn paper products. Image analysis techniques are employed, supported by a specially constructed darkroom platform to ensure uniform light intensity for capturing photographs. An automated program is developed for the detection and evaluation of wrinkle characteristics and distribution, which allows the free choice of the region to be detected and the representation of the wrinkle geometry not limited by the number. To enhance the precision of this program, the ellipticity is initially rectified for products without flanges, specifically cup-shaped deep-drawn products. The ellipticity is caused by the pronounced springback effect of the paperboard. The approach is employed to investigate the impact of material properties, blank holder force, drawing depth, drawing clearance, and punch speed on wrinkling formation after the deep-drawing process. The findings reveal that the blank holder force and drawing clearance are critical factors in wrinkle formation, with higher blank holder force generally leading to increased wrinkle numbers.

Effect of Material Extrusion Process Parameters to Enhance Water Vapour Adsorption Capacity of PLA/Wood Composite Printed Parts

This study aims to optimise the water vapour adsorption capacity of polylactic acid (PLA) and wood composite materials for application in dehumidification systems through material extrusion additive manufacturing. By analysing key process parameters, including nozzle diameter, layer height, and temperature, the research evaluates their impact on the porosity and adsorption performance of the composite. Additionally, the influence of different infill densities on moisture absorption is investigated. The results show that increasing wood content significantly enhances water vapour adsorption, with nozzle diameter and layer height identified as the most critical factors. These findings confirm that composite materials, especially those with higher wood content and optimised printing parameters, offer promising solutions for improving dehumidification efficiency. Potential applications include heating, ventilation, and air conditioning systems or environmental control. This work introduces an innovative approach to using composite materials in desiccant-based dehumidification and provides a solid foundation for future research. Further studies could focus on optimising material formulations and scaling this approach for broader industrial applications.

Personalized 3D Printing of Artificial Vertebrae: A Predictive Bone Density Modeling Approach for Robotic Cutting Applications

Robotic vertebral plate cutting poses significant challenges due to the complex bone structures of the lumbar spine, which consist of varying densities in cortical and cancellous regions. This study addresses these challenges by developing a predictive model for robotic vertebral plate cutting force and bone quality recognition through the fabrication of artificial vertebrae with controlled, consistent bone density. To address the variability in bone density between cortical and cancellous regions, CT data are utilized to predict target bone density, serving as a foundation for determining the optimal 3D printing process parameters. The proposed methodology integrates a Response Surface Methodology (RSM), Back Propagation (BP) neural network, and genetic algorithm (GA) to systematically evaluate the effects of key process parameters, such as the filling density, material flow rate, and layer thickness, on the printed vertebrae’s density. A one-factor experimental approach and RSM-based central composite design are applied to build an initial bone density prediction model, followed by Sobol’s sensitivity analysis to quantify the influence of each parameter. The GA-BP neural network model is then employed to rapidly and accurately identify optimal printing parameters for different bone layer densities. The resulting optimized models are used to fabricate personalized artificial lumbar vertebrae, which are subsequently validated through robotic cutting experiments. This research not only contributes to the advancement in personalized 3D printing technology but also provides a reliable framework for developing patient-specific surgical planning models in robot-assisted orthopedic surgery.

Raman‐Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical Saccharomyces cerevisiae Fermentations

ABSTRACTRaman spectroscopy, a robust and non‐invasive analytical method, has demonstrated significant potential for monitoring biopharmaceutical production processes. Its ability to provide detailed information about molecular vibrations makes it ideal for the detection and quantification of therapeutic proteins and critical control parameters in complex biopharmaceutical mixtures. However, its application in Saccharomyces cerevisiae fermentations has been hindered by the inherent strong fluorescence background from the cells. This fluorescence interferes with Raman signals, compromising spectral data accuracy. In this study, we present an approach that mitigates this issue by deploying Raman spectroscopy on cell‐free media samples, combined with advanced chemometric modeling. This method enables accurate prediction of protein concentration and key process parameters, fundamental for the control and optimization of biopharmaceutical fermentation processes. Utilizing variable importance in projection (VIP) further enhances model robustness, leading to lower relative root mean squared error of prediction (RMSEP) values across the six targets studied. Our findings highlight the potential of Raman spectroscopy for real‐time, on‐line monitoring and control of complex microbial fermentations, thereby significantly enhancing the efficiency and quality of S. cerevisiae‐based biopharmaceutical production.

Crack analysis in laser powder bed fusion-fabricated 2195 Al-Li alloy: An in-depth examination of influential variables

Laser powder bed fusion (LPBF) of high-strength 2195 Al-Li alloys offers significant industrial potential, yet the process is hindered by a high susceptibility to hot cracking. This study systematically investigates the hot cracking mechanisms and the key processing parameters influencing crack formation in LPBF-fabricated 2195 Al-Li alloys. Extensive experimental analysis revealed that crack density is significantly affected by scanning velocity and laser power, with scanning velocity identified as the most critical factor through a neural network model and a crack susceptibility model. The study demonstrates that achieving crack-free samples requires extremely low scanning velocities, as higher velocities exacerbate crack formation, particularly at high-angle grain boundaries (HAGBs). The findings also highlight the limitations of preheating in reducing crack susceptibility, emphasizing the importance of precise control over processing parameters to mitigate cracking in LPBF-fabricated 2195 Al-Li alloys.

Tunable diameter of electrospun fibers using empirical scaling laws of electrospinning parameters

This study introduces a new semi-empirical power-law model for predicting electrospun fiber diameter (D), addressing key processing parameters. Polycaprolactone (PCL) fibers were produced using a solvent mixture of Trichloromethane (TCM), Dimethyl Formamide (DMF), and ethanol (EtOH). Systematic experiments validated an existing theoretical model and led to the development of a novel model: D ∼ (c1/2η1/3Q1/5X2/3)/(U2/3ω1/4I1/5). This model incorporates seven crucial parameters: viscosity (η), concentration (c), voltage (U), spinning distance (X), flow–rate (Q), current (I) and collector wheel rotation speed (ω). The model was validated through a partial factorial design experiment, proving to be a valuable and reliable tool for predicting fiber diameters and optimizing electrospinning processes. The ability to control fiber diameter is essential for tailoring electrospun fibers for various applications, including biomedicine, filtration, sensors, and lightweight materials.

Monitoring small-scale bioreactor studies for media development using polarized total synchronous fluorescence spectroscopy (pTSFS) and synchronous light scattering (SyLS)

Biopharmaceutical process development often involves the use of small-scale bioreactors (SSBR) for optimizing media formulations and process conditions during scale up to commercial scale production. Two key process parameters (CPP) used in SSBR studies are protein titre and viable cell density (VCD). Here, we explore the efficacy of parallel polarized total synchronous fluorescence spectroscopy (TSFS||) and Synchronous Light Scattering (SyLS||) to qualitatively monitor these CPPs and quantitatively predict titre and VCD for a large-scale cell culture media optimization SSBR study. The study involved 71 different media formulations (50+ components each), and the bioprocess was run for 13 days or more. Samples were extracted at set times (Day 0, 3, 9, and 13) and clarified by centrifugation. TSFS|| spectra showed significant emission changes along with increased light scatter over the course of the bioprocess. SyLS|| measurements strongly correlated with particle size data obtained from Dynamic Light Scattering but did not correlate well with VCD probably because of the centrifugation-based sample preparation. Statistical and principal component analysis (PCA) of the pTSFS data showed that spectral variation was greater between media formulations than due to the evolving bioprocess. This prevented the development of accurate global prediction models for media performance (e.g., predicting product titre at day 9 from media spectra measured at day 0). However, classification methods were successfully used to select media subsets with better quantitative prediction accuracy based on spectral similarities. A practical binary (high/low performance) classification model based on Support Vector Machines was generated for media formulation screening. Combining emission and scatter measurements with multivariate data analysis provides a more holistic, multi-attribute bioprocess monitoring method that minimizes the need to use different offline analytical methods. This methodology can be used to monitor process trajectories and deviations, and ultimately be used to predict bioprocess CPPs when implemented on production scale processes where there is much less compositional variation in the media. We believe this SSBR-pTSFS/SyLS approach will provide a valuable resource to develop the design/parameter space for in-process monitoring at production scale from early-stage process/media development studies.

Autoregressive distributed lag-based dynamic uniformity modeling and monitoring approaches for superconductor manufacturing

ABSTRACT High-temperature superconductors (HTS), known for their high efficiency and low energy loss, have found profound applications across various fields, driving the demand for long, uniformly performing tapes. However, ensuring uniform performance over extended lengths of HTS tapes, often characterized by the consistency of critical current, remains challenging due to fluctuations in growth conditions during manufacturing. To elucidate the mechanisms underlying variations in tape uniformity and enable real-time monitoring of associated parameters, we propose an Autoregressive Distributed Lag (ADL)-based Dynamic Uniformity Modeling and Monitoring (ADUM2) approach. This method integrates uniformity measurement, the identification of critical process parameters and real-time monitoring within the manufacturing process. The ADUM2 approach is applied to the advanced metal organic chemical vapor deposition (A-MOCVD) process, a pilot-scale method for superconductor manufacturing. Our model demonstrates superior performance compared to benchmark methods, accounting for over 80% of the total variance in the data and identifying 13 key process parameters influencing the uniformity of HTS tapes. This study offers significant insights into the high-temperature superconductor manufacturing process and holds the potential to facilitate the production of cost-effective, uniformly performing long superconducting tapes in the future.

Statistical Evaluation of Flotation Behavior of Chalcopyrite in the Presence of SIPX and Acetoacetic Acid n-Octyl Ester as a Novel Collector Blend: A Sustainable Approach

The chalcopyrite deposit in Malanjkhand, India, is the largest copper ore producer in the country. However, its flotation performance has room for improvement. This study used standard flotation experiments using a mechanical flotation cell using a conventional collecting reagent and a collector blend consisting of xanthate and ester. A three-factor, three-level Box-Behnken design was used to statistically evaluate the experimental design. The obtained data were analyzed using an ANOVA, cubic plots, and response surface methods. The goal was to evaluate the effects and interactions of three key process parameters in chalcopyrite flotation: the dosages of sodium silicate (depressant), sodium isopropyl xanthate (collector), and acetoacetic acid n-Octyl ester (co-collector/modifier). The results implied that introducing acetoacetic acid n-Octyl ester along with minor tweaks in the dosages of sodium isopropyl xanthate helped increase copper grades by at least 15%, with good recovery percentiles. Among the three parameters tested, the copper’s grade and recovery were considerably positively influenced by the AoE dosage in the collector blend. Employing 0.006 kg/t sodium silicate and 0.0065 kg/t sodium isopropyl xanthate with 0.005 kg/t of acetoacetic acid n-Octyl ester, an optimum copper recovery of 88.87% could be achieved. However, with a sodium silicate dosage of 0.0048 kg/t, a SIPX dosage of 0.008 kg/t, and an AoE dosage of 0.005 kg/t, optimized copper grade (1.55%) could be achieved, which is a 78.1% increase from the feed sample grade. To validate the expected results, verification experiments were carried out, and the experimental findings were found to be on at par with the statistical model predictions. Furthermore, the calculated SPI value of 0.0000147cap Kg−1 for a global index per resident lies between 0 and 1.

Multi-Stage Corn-to-Syrup Process Monitoring and Yield Prediction Using Machine Learning and Statistical Methods.

Corn syrup is a cost-effective sweetener ingredient for the food industry. In producing syrup from corn, process control to enhance and/or maintain a constant dextrose equivalent value (DE) is a constant challenge, especially in semi-automated/batch production settings, which are common in small to medium-size factories. Existing work has focused on continuous process control to keep parameter values within a setpoint. The machine learning method applied is for time series data. This study focuses on building process control models to enable semi-automation in small to medium-size factories in which the data are not as time dependent. Correlation coefficients were used to identify key process parameters that contribute to feed pH value and DE. Artificial neural network (ANN), support vector machine (SVM), and linear regression (LR) models were built to predict feed pH and DE. The results suggest (1) model accuracy ranges from 91% to 96%; (2) the ANN models yielded about 1% to 3% higher accuracy than the SVM and LR models and the prediction accuracy is robust even with as few as six data sets; (3) both the SVM and ANN models have noise tolerant properties, but ANN has a higher noise tolerance than SVM; (4) SVM performance can be hindered when using high-dimensional data sets; (5) the LR model yields higher variation in accuracy prediction than ANN and SVM; (6) distribution fitting is a good approach for generating data; however, fidelity of fitting can greatly impact accuracy; and (7) multi-stage models yield higher accuracy than single-stage models, but there are pros and cons to each approach.