Effect Of Process Variables Research Articles

Thermodynamic assessment of tin-smelting from cassiterite concentrates

ABSTRACT Tin is a critical and rare metal produced via carbothermic reduction smelting of upgraded concentrates in two steps, primary and secondary reduction. There is limited literature available on cassiterite smelting and the thermodynamic behaviour of Sn during primary and secondary reduction processes. The present work performs a systematic assessment/optimisation of smelting parameters of a simplified cassiterite concentrate for the first time. This assessment was carried out with the aid of thermochemical analysis. The effect of process variables including reduction extent, temperature, and flux addition on the outputs of the primary and secondary reduction steps was studied. The effects of temperature and slag composition on slag liquidus temperature and viscosity were determined. The effect of recycling the process byproduct, the hard head (HH), on operational parameters and outcomes, especially Sn recovery, was also investigated, and a series of optimum process conditions was proposed.

Role of support bio-templating in Ni/Al2O3 catalysts for hydrogen production via dry reforming of methane

Bio-templating, a synthetic approach inspired by nature, is an emerging area in material engineering. In this study, waste leaves of Sycamore were utilized as a bio-template for producing alumina support to prepare catalyst. The performance of Ni and Ce impregnated on bio-templated alumina support was investigated in dry reforming of methane for the first time. The effect of process and catalytic variables were examined in detail. The results showed that impregnation of 20% Ni and 3% Ce on the bio-templated alumina led to improved Ni dispersion and achieving the maximum CH4 conversion of 88.7%, CO2 conversion of 78.5%, and H2 yield of 85.3%, compared to 84.4%, 75.6% and 83.4% for the non-templated catalyst at 700 °C, respectively. Detailed characterization of the catalysts revealed that the enhanced performance in the bio-templated catalyst could be attributed to smaller Ni particles, superior dispersion of Ni on the support, the mesoporous structure of alumina, and the larger surface area of support. Furthermore, analysis of the used catalyst showed reduced coke formation on the catalyst surface and high stability of bio-templated catalysts, highlighting the main advantage of bio-templated catalysts over non-templated ones. The findings presented in this study contribute to the potential future applications of bio-templating materials and shed light on the rational design of bio-templating materials.

An Energy-Efficient Generic Accuracy Configurable Multiplier Based on Block-Level Voltage Overscaling

Voltage Overscaling (VOS) is one of the well-known techniques to increase the energy efficiency of arithmetic units. Also, it can provide significant lifetime improvements, while still meeting the accuracy requirements of inherently error-resilient applications. This paper proposes a generic accuracy-configurable multiplier that employs the VOS at a coarse-grained level (block-level) to reduce the control logic required for applying VOS and its associated overheads, thus enabling a high degree of trade-off between energy consumption and output quality. The proposed configurable Block-Level VOS-based (BL-VOS) multiplier relies on employing VOS in a multiplier composed of smaller blocks, where applying VOS in different blocks results in structures with various output accuracy levels. To evaluate the proposed concept, we implement 8-bit and 16-bit BL-VOS multipliers with various blocks width in a 15-nm FinFET technology. The results show that the proposed multiplier achieves up to 15% lower energy consumption and up to 21% higher output accuracy compared to the state-of-the-art VOS-based multipliers. Also, the effects of Process Variation (PV) and Bias Temperature Instability (BTI) induced delay on the proposed multiplier are investigated. Finally, the effectiveness of the proposed multiplier is studied for two different image processing applications, in terms of quality and energy efficiency.

Effect of Process Variables on Interface Friction Characteristics in Strip Drawing of AA 5182 Alloy and Its Formability in Warm Deep Drawing

Warm forming is widely used to enhance the formability of aluminum alloy sheets. In warm deep drawing, the process variables significantly affect frictional characteristics at the tool–blank interface. It has been a conventional approach to use a constant value of friction coefficients in the finite element (FE) simulations. However, this can occasionally result in suboptimal accuracy of the predictions. In the present work, strip drawing tests were carried out on AA5182 aluminum alloy sheets to investigate the effect of important process variables, namely, temperature, contact pressure, and drawing speed, on the friction coefficient in the warm forming temperature range (100–250 °C) under lubricated condition. The results obtained from the strip drawing tests were used for defining the friction conditions in the simulation of warm deep drawing of cylindrical cups incorporating the variation of the friction coefficient with contact pressure and speed at different temperatures. The Barlat89 yield criterion was used to define the effect of anisotropy in the material. The Voce hardening law and Cowper–Symonds model were used to incorporate the effect of strain hardening and strain rate, respectively, in the simulation. Drawability and peak force were compared with the predictions when a constant friction coefficient was assumed. Warm deep drawing experiments were conducted to validate the predicted drawability and load–displacement curves. It is clearly observed that the accuracy of prediction of the limiting drawing ratio and peak load through simulations is improved by incorporating the effect of pressure and speed on friction coefficient as it captures the local variations of friction during warm deep drawing precisely, rather than assuming a constant average friction coefficient at all the tool–blank contact areas.

Experimental investigation on the effect of process variables for the quality characteristics of AA 2024 processed in cold extrusion

Extrusion process has many applications in manufacturing industries due to its ability to produce products of high quality. Extrusion process can be classified into hot extrusion and cold extrusion. The cold forward extrusion is carried out at ambient temperature and has the additional benefit of improved mechanical characteristics. The metarial is compressed under intense pressure through a die orifice with a specific shape during the extrusion process. This process is effected by a few process variables, including die angle, punch speed, and lubrication are in greater extent towards the extrusion force requirement, microstructure and the product quality. Hence, the present experimental work focuses on extrusion of circular billet to produce cylindrical rod. Studying the behaviour of the material and the importance of the input process parameters during the cold extrusion process is the primary goal of this work. The experiments are carried out with AA 2024 alloy because of its wide applications in navy and aircraft structures. The varying die angles (10°, 20° & 30°) as well as punch speed (1.6 mm/min, 3.2 mm/min and 4.8 mm/min) and lubricants (molybdium sulphide (MoS2), zinc stearate and grease) chosen as input parameters. The out put responses of this extrusion process are extrusion force, displacement, time and surface roughness. Extrusion forces are calculated based on flow stress curves at the locations of greatest elastic deformation. The results shows that increasing the punch speed and die angle increases the extrusion force. The microstructure evolutions and grain refinement at different die angles are examined using electron back scatter diffraction analysis. At 30° die angle, the microstructure showed grain refinement. It is also noted that the damage is significant at 30° die angle with a punch speed above 4.8 mm/min.

Process variability effects on tensile response in injection molded, fluorinated thermoplastics

AbstractThe mechanical properties of fluorinated thermoplastics (i.e., tensile strength and elongation) can vary with changes in injection molding processing parameters. Four fluoropolymers are examined: poly(vinylidene fluoride) (PVDF) and random poly(vinylidene fluoride‐co‐chlorotrifluoroethylene) (PVDF‐CTFE) with three CTFE concentrations. Dog bones were manufactured with various cylinder dwell times and mold cooling times to assess the manufacturing sensitivity to the tensile response. Dwell and cooling times increasingly impact mechanical performance as CTFE concentration increases. Specimens exhibit higher tensile strength as a function of injection order. The first injected specimen exhibits the lowest tensile strength and highest elongation in all copolymers. This trend becomes more pronounced among fluoropolymers with higher CTFE concentration and lower weight‐averaged molecular weight. Parallel plate rheology was used to obtain the zero‐shear viscosity as a function of material type, process, and injection order. We found that in the copolymers, the first injected sample exhibited a lower zero‐shear viscosity than the next, which indicates a lower molecular weight in the first injected specimen. This phenomenon was not presented for the PVDF homopolymer. Copolymer mechanical uncertainties are hypothesized to result from the shorter molecular weight chains extruding out of the specimens' sides as a flash due to higher mobility with CTFE segments.

Thermostable keratinase from Bacillus cereus L10: optimization and some potential biotechnological applications

AbstractThe accumulation of feather waste is a critical problem in solid waste management; therefore, microbial management of waste is highly significant. Consequently, the keratinolytic capacity of some bacteria isolated from the keratinous waste area was evaluated. The most promising isolate coded as L10 showed the highest feather degradation efficiency 72.7% was identified as Bacillus cereus L10 using 16S rRNA gene sequencing. The effect and contribution of independent process variables were studied by Plackett–Burman design. The optimal values of the independent variables obtained from the Plackett–Burman design were chicken feather, 1%; K2HPO4, 0.3%; KH2PO4, 0.05%; MgSO4 7H2O, 0.01%; yeast extract, 0.05%; inoculum size 4% with pH 7 and incubation period 5 days at 35 °C. The optimal parameters were validated resulting in the enhancement of keratinase production (9.602 U/ml), protein concentration (521.17 µg/ml), and feather degradation (94%) along with a 4.56-fold increased of keratinase production. The enzyme is relatively thermostable, completely stable between 30 and 80 °C for 1 h and retained 97.66% of its activity at 100 °C. B. cereus L10 was able to degrade different types of keratins. Interestingly, the feather hydrolysate from optimized keratinase production medium showed plant growth promoting activity by producing indole-3-acetic acid (IAA). The feathers hydrolysate produced a great benefit by its use as a soil biofertilizer through enhancing the germination of Triticum aestivum. The important implication of keratinase in laundry detergent and leather industry was examined. Results revealed that the enzyme achieved great efficiency in removing protein and blood-rich stains within 30 min at 36 °C in addition to its thoroughly dehairing of cow-hide after 40 h incubation without any damage. The dehaired pelt exhibited a smooth, velvety, and white color surface, good flexibility. Therefore, our study presents an effective method to turn waste into wealth. Graphical Abstract

A fully non-volatile reconfigurable magnetic decoder

In recent years, emerging technologies like spintronics are gaining importance for their ability to tackle issues pertaining to CMOS like high leakage effect, complex data backup operation, and power gating. In this regard, Magnetic Tunnel Junction (MTJ), the most prominent spin device is chosen for our application. A fully Non-Volatile Reconfigurable Magnetic Decoder (NVRMD), using fully NVR NAND/AND and fully NVR NOR/OR logic gates is presented in this paper, following the Logic-In-Memory (LIM) architecture in attempts to mitigate the von Neumann Bottleneck. The proposed designs are superior to their contemporaries in terms of low power, less delay, and dynamic reconfigurability. Reduction in read energy by 79% and write energy by 28% has been achieved in the proposed NVR NAND/AND NOR/OR circuit compared to its counterpart. Corner analysis is performed to establish the robustness of the circuit under extreme conditions in CMOS and reliability analysis is done to understand the effect of process variations of MTJ parameters on the circuit performance.

Analyzing the occurrence of foaming in batch fermentation processes using multiway partial least square approaches

AbstractThis article presents an application of multiway partial least squares (MPLS) methods to develop interpretative correlation models to monitor the foaming occurrence and improve batch fermentation. We choose the exhaust differential pressure as a quality variable to quantify the foaming occurrence and consider three‐dimensional datasets of different batches, process variables, and measurements. We integrate batch‐wise unfolding (BWU) and observation‐wise unfolding (OWU) of plant datasets with standard, dynamic, and kernel PLS methods. We find that dynamic PLS (DPLS) with OWU and time‐lagged quality variables to be the most efficient, accurate, and easy to implement. The BWU approach is useful for analyzing the differences between batches and identifying abnormalities and outliers, while the OWU quantifies the variation within a given batch. With OWU, the DPLS method with one unit of time lag in the quality variable is the most effective, accurate, and easy to implement. With both BWU and OWU, we identify the quantitative effects of process variables on the quality variable and providence guidance to improve fermentation performance.

Prediction of compressive strength in additively fabricated part using long short term memory based neural network

With ability to produce complex geometries and accurate parts, additive manufacturing (AM) could be applied in various sectors including aerospace and biomedical, though the frequent occurrence of defects and unavailability of feasible monitoring and control system prohibits their effective utilization. Technological developments and use of artificial intelligence, machine learning models could be a perfect solution to the problems. In this study, a predictive model is developed using deep learning (DL) algorithm and investigated the compressive strength of fused deposition modelling (FDM) printed part. An improved recurrent neural network (RNN) model called long short-term memory (LSTM) is used for predicting the compressive strength of part produced by FDM process. Model developed with LSTM utilizes layer-wise relevance propagation (LRP) algorithm to establish relation between the FDM part and LSTM layers. Predictive model developed in this study uses the back propagation algorithm to establish the relation between the compressive strength of part and input process variables. A total of 81 poly lactic acid samples were printed and tested, out of which 57 samples were used for training the model and 16 data were used for validating the model and remaining data were used for testing the model. Effects of process variables including infill density, print orientation, raster orientation and layer thickness have been analyzed. The values obtained from the predictive model was in good agreement with experimental results along 1% error. LSTM based DL model predicted the compressive strength closer to the measured values. From the results obtained, the developed model holds good for predicting the compressive strength of FDM printed part.

Optimizing the effect of ultrasonication and germination on antinutrients and antioxidants of kodo (Paspalum scrobiculatum) and little (Panicum sumatrense) millets.

Kodo (Paspalum scrobiculatum) and little (Panicum sumatrense) millet grains were utilized to minimize their antinutrient content (phytate and tannin) and maximize their antioxidant activity (DPPH) by studying the effect of ultrasonication time, germination time and temperature using central composite rotatable design. Results revealed the optimum conditions for producing ultrasonicated and germinated kodo and little millet flour of the highest antioxidant activity and lowest antinutrient content (phytate and tannin) by using 30min for ultrasonication, 72h for germination at 40°C. Further, a second order model was developed to describe and predict the effect of process variables on antioxidant activity and antinutrient contents. Extended experiments were carried out under the optimized conditions to validate the developed model. The antioxidant activity obtained was 88.46% RSA and 89.06% RSA for kodo and little millet grain flours, respectively whereas antinutrient content for phytate was 0.165mol/kg and 0.199mol/kg and for tannin 2.88mol/kg and 9.51mol/kg, for kodo and little millet grain flours, respectively. This study provides useful information about the potential utilization of ultrasonicated and germinated kodo and little millet grain flours for the development of functional foods.

Effects of wet granulation process variables on the quantitative assay model of transmission Raman spectroscopy for pharmaceutical tablets

Transmission Raman spectroscopy (TRS) is a process analytical technology tool for nondestructive analysis of drug content in tablets. Although wet granulation is the most used tablet manufacturing method, most TRS studies have focused on tablets manufactured via direct compression. The effects of upstream process parameter variations, such as granulation, on the prediction performance of TRS quantitative models are unknown. We evaluated the effects of process parameter variations during granulation on the prediction performance of the TRS quantitative model. Tablets with a drug concentration of 1%w/w were used. We developed PLS calibration models for the drug concentration range of 70–130% label claims. Subsequently, we predicted the drug content of the tablets with different granulation parameters. The results of our study demonstrate that the variation in the predicted recovery due to the variation in granulation parameters was practically acceptable. The calibration model showed a good prediction performance for tablets manufactured at different granulation scales and thicknesses. Therefore, we conclude that TRS quantitative models are robust to variations in upstream processes, such as granulation and downstream variations in tableting parameters. These results suggest that TRS is a versatile non-destructive quantitative analysis method that can be applied in tablet manufacturing.

Improving the Performance of CNN Accelerator Architecture under the Impact of Process Variations

Convolutional neural network (CNN) accelerators are popular specialized platforms for efficient CNN processing. As semiconductor manufacturing technology scales down to nano scale, process variation dramatically affects the chip’s quality. Process variation causes delay variation within the chip due to transistor parameter differences. CNN accelerators adopt a large number of processing elements (PEs) for parallel computing, which are highly susceptible to process variation effects. Fast CNN processing desires consistent performance among PEs; otherwise the processing speed is limited by the slowest PE within the chip. In this work, we first quantitatively model and analyze the impact of process variation on CNN accelerators’ operating frequency. We further analyze the utilization of CNN accelerators and the characteristics of CNN models. We then leverage the PE underutilization to propose a sub-matrix reformation mechanism and leverage the pixel similarity of images to propose a weight transfer technique. Both techniques are able to tolerate the low-frequency PEs and achieve performance improvement at chip level. Furthermore, a novel resilience-aware mapping technique that exploits the diversity in the importance of weights is also proposed to improve the performance. Evaluation results show that our techniques are able to achieve significant processing speed improvement with negligible accuracy loss.

Response Surface Methodology (RSM) Optimization of Pulsed Electric Field (PEF) Pasteurization Process of Milk-Date Beverage

Milk beverage with added natural sweetener is well appreciated by consumers as a nutritious and healthy product with unique sensorial quality attributes. However, this product requires a suitable pasteurization method without significant impact on the sensorial and physicochemical quality characteristics of the product. This study optimizes the pulsed electric filed (PEF) conditions for the pasteurization of a milk-date beverage with conserved physicochemical quality properties. The effect of process variables, such as pulse off time (20, 30, and 40 μs), number of pulses (20, 50, and 80), powder ratio (10, 15, 20, and 25% w/w), storage time (2, 4, and 6 days), and storage temperature (5, 15, and 25 °C) on the responses of total viable count (TVC), color difference (∆E), pH, and total soluble solids (TSS) was evaluated using the RSM central composite design (CCD). Pulse off time, number of pulses, date powder/milk ratio (w/w), storage time, and storage temperature greatly impacted the microbial and physical properties of the beverage. The optimal conditions for decreasing the microbiological load and physical change of beverages were a pulse off time of 40 μs, number of pulses of 80, and storage temperature of 5 °C for all powder ratios. These variables gave a safe beverage for up to six days. At optimal conditions, the values of pH, TSS, ∆E, and TVC were 7.51, 15.44 °Brix, 18.01, and 0.138 Log 10 CFU/mL, respectively, for the powder ratio of 10% (w/w); 7.66, 18.6 °Brix, 21.46, and 0.284 Log 10 CFU/mL, respectively, for the powder ratio of 15% (w/w); 7.56, 21.52 °Brix, 25.24, and 0.577 Log 10 CFU/mL, respectively, for the powder ratio of 20% (w/w); and 7.2, 24.2 °Brix, 29.34, and 0.741 Log 10 CFU/mL, respectively, for the powder ratio of 25% (w/w).

Numerical optimization of hydrothermal liquefaction process for the production of bio-oil and bio-char from Citrus maxima peel waste and product characterization.

This research study aims to potential utilization of Citrus maxima peel waste and optimize the hydrothermal liquefaction process for the production of bio-oil (BO) and bio-char (BC). The effect of several HTL processing variables on BO yield (%) and BC yield (%), including temperature, retention period, and slurry concentration, has been examined using central composite design (CCD) (a three-level three-factor design). The optimized values of HTL process variables were found to be 240°C (temperature), 52min (retention time), and 7% (slurry concentration) and the corresponding responses were 5.794% (BO yield) and 29.450% (BC yield). The values obtained from the RSM-CCD model as the predicted values agreed with the experimental values (5.93% and 30.14%). Further the BO and BC obtained under optimized conditions and CPP were analyzed to identify the variations by 1H-NMR, GC-MS, FT-IR, and CHNO-S.

High-Dimensional Data Global Sensitivity Analysis Based on Deep Soft Sensor Model.

This article investigates the sensitivity analysis (SA) of high-dimensional data to identify the effects of process variables on output quantity of interest (QoI) in industrial soft sensor modeling. The computational cost of analyzing the SA of high-dimensional data is high, and models available for SA techniques usually have limited generalization capacity. Therefore, we propose a novel high-dimensional data global SA (GSA) approach based on a deep soft sensor model to address these issues. We first develop an approximately incremental grouping (AIG) algorithm and a region-based cooperative co-evolution (RBCC) algorithm to decompose the high-dimensional data into independent regions for the GSA. Subsequently, a multihead deep soft sensor model with generalization performance is designed to determine the GSA indices of each decomposed region. Specifically, the region of interest (RoI) align algorithm provides the multihead with precisely located decomposed region features. Finally, based on the uncertainty analysis of each model head, we present a joint loss function with the Monte Carlo dropout (MC-dropout) algorithm to measure the GSA indices of each decomposed region on QoIs. Experimental evaluation results on a benchmark dataset and a real-world one demonstrate the effectiveness of the proposed approach in addressing the GSA of high-dimensional data in industrial processes.

Geometrical and crystal plasticity modelling: Towards the establishment of a process-structure-property relationship for additively manufactured 316L struts

This paper presents a methodology to establish a process structure property (PSP) relationship for the additive manufacturing (AM) of thin AISI 316L struts, as might be used in coronary stent applications. The methodology is based on a new geometrically based process-structure method for AM process variables, and crystal plasticity finite element (CPFE) modelling that includes the representation of melt pool microstructure morphology and texture. The effects of AM process variables are characterised with respect to ductility, yield strength and UTS across a range of process variables, including hatch spacing, layer thickness and build orientation (two orientations are considered: horizontal and vertical). The CPFE-predicted effect of build orientation is shown to be consistent with experimental test results. The present methodology has allowed identification of optimal hatch spacing and layer thickness for a given laser spot size, power and scanning speed. CPFE modelling of AM melt pool texture is shown to be key to successful prediction of the effects of process variables on mechanical behaviour.

Understanding and modelling the aquafeed extrusion process for Atlantic salmon feed with an empirical model

An empirical model was applied to quantitatively model the effects of extrusion variables and recipe compositions on quality of feed pellets. Lab extrusion trials were performed with an Atlantic salmon feed recipe with using a central component design (CCD). Comparing with a coated commercial feed made with the same recipe, the lab extrusion pellets had similar bulk density, hardness, and durability properties. A Response Surface Method (RSM) was also used to regress the extrusion trial data and compared with the empirical model results. The respective Absolute Average Deviation (AAD %) values of the empirical model and RSM regression were 4.1 % and 1.8 % for pellet bulk density, 7.4 % and 4.9 % for pellet hardness, 10.4 % and 10.0 % for pellet oil absorption, 13.7 % and 8.3 % for pellet water stability, and 11.3 % and 5.5 % for pellet durability. Using the model coefficients obtained from the extrusion results, the empirical model can partly extrapolate the pellet qualities of an additional extruded recipe, which has a slightly adjusted wheat gluten and wheat. The model performance was evaluated based on. R2, RSME (root-mean-square deviation), AAD % of the empirical model was better than the RSM model to predict the effects of extrusion variables on pellet quality parameters. The effects of the extrusion process variables and recipe composition on pellet quality parameters are predicted by the empirical model. Its demonstrates that the empirical model is a new method to understand extrusion trial results in different extrusion systems and recipes without changing its mathematical form of expression, which gives a common reference to compare an extruded recipe in different systems and has practical engineering applications.

Application of Design of Experiment in the Optimization of Apixaban-Loaded Solid Lipid Nanoparticles: In Vitro and In Vivo Evaluation

Solid lipid nanoparticles (SLnPs) are usually utilized as lipid-based formulations for enhancing oral bioavailability of BCS class IV drugs. Accordingly, the objective of this work was to investigate the effect of formulation and processing variables on the properties of the developed SLnPs for oral delivery of apixaban. Randomized full factorial design (24) was employed for optimization of SLnPs. With two levels for each independent variable, four factors comprising both formulations and processing factors were chosen: the GMS content (A), the Tween 80 content (B), the homogenization time (C), and the content of poloxamer 188 used (D). The modified hot homogenization and sonication method was employed in the formulation of solid lipid nanoparticles loaded with apixaban (APX-SLnPs). The size of APX-SLnPs formulations was measured to lie between 116.7 and 1866 nm, polydispersity index ranged from 0.385 to 1, and zeta potential was discovered to be in the range of − 12.6 to − 38.6 mV. The entrapping efficiency of APX-SLnPs formulations was found to be in the range of 22.8 to 96.7%. The optimized formulation was evaluated in vivo after oral administration to rats. Oral administration of APX-SLnPs resulted in significant prolongation in bleeding time compared with both positive and negative control. This indicates the ability of this system to enhance drug therapeutic effect either by increasing intestinal absorption or trans-lymphatic transport. So, this study highlighted the capability of SLnPs to boost the pharmacological effect of apixaban.Graphical

Investigation of process variable effects on palm kernelamidopropyl betaine production

Abstract Cocamidopropyl betaine is a coconut oil-based surfactant that is widely used in cosmetics and personal care products due to its milder and less irritating properties compared to conventional petroleum-based surfactants. Palm kernel oil can be used for the synthesis of betaine (palm kernel amidopropyl betaine), following the same route as for the synthesis with coconut oil via two reaction steps. However, there was little information on the optimal operating conditions for each process. The method for the production of betaine and its operating conditions were studied in order to obtain a compatible betaine product with better properties than those of the commercial product. The studied method and operating conditions allowed the production of low viscosity betaine with a higher surfactant content (31.55 wt%) than that of the commercial betaine products (25.10 wt%). In addition, other feedstocks such as coconut oil, split palm kernel oil and coconut oil-based fatty acid methyl ester were used in this study to produce betaine from low-cost production.