Process Variables Research Articles

Post-combustion carbon capture process modeling, simulation, and assessment of synergistic effect of solvents

Post-combustion carbon capture appears to be a promising solution to reduce the emission of carbon dioxide (CO2) from power plants that generate electricity using either coal or natural gas. In addition, carbon capture process efficiency, capacity, and energy consumption have become challenging against the performance of the capture process. However, synergistic effect due to solvents blend has gained attention to reduce the process energy consumption and enhance process efficiency. In this study, blends of methyldiethanolamine (MDEA) and piperazine (PZ) at different concentrations were investigated using a validated post-combustion capture process model using Aspen HYSYS. Results were compared with 30 wt% monoethanolamine (MEA) as reference case. The effective process variables are concentration of solvents, the amount of water and solvent in the makeup section, viscosity of solvent, energy consumed in different process stages, and the amount of lean solvent flow rate. These variables were studied against fixed process variables using rate-based model. The study shows that using (43 wt% MDEA/7 wt% PZ) for post-combustion carbon capture needs 2.53 MJ/kgCO2 regeneration energy for 88.5% process efficiency compared to 4.003 MJ/kgco2 for 30 wt% MEA without the need for any process modifications. In addition, it was found that solvents synergistic effect contributes to resolving the drawbacks of post-combustion capture that will enable the high utilization of the process and contribution to reduce the consequences effect of climate change. Therefore, the study will help policymakers, industries and encourage researchers towards the large-scale commissioning of blended solvent -based post-combustion capture process.

Activity, stability, and kinetic study of CuO/TiO2 Janus photocatalyst for Rhodamine B degradation

ABSTRACT The CuO/TiO2 photocatalyst was synthesized using hydrothermal and sonochemical methods and subsequently applied to degrade rhodamine B in wastewater. The best synthesis conditions were established, determining Cu(NO3)2 solution concentration of 2.5 wt.% and calcination temperature of 500 °C. The CuO/TiO2 photocatalyst underwent characterization through various techniques, including ICP, photoluminescence, X-ray diffraction, ASAP, scanning electron microscope (SEM), EDAX, transmission electron microscopy (TEM), and diffuse reflectance spectroscopy. TEM and SEM analyses revealed the presence of TiO2 nanowires and CuO nanoparticles. The Box–Behnken design, encompassing 27 experimental runs, assessed the impact of process variables such as initial dye concentration, pH, UV lamp power, and catalyst dose on the degradation process. The model's R2 value of 0.9893 indicated a high precision in fitting the predicted data to their actual values. Analysis of variance results highlighted UV irradiation power as the most significant variable within the design space. In addition, the CuO/TiO2 photocatalyst demonstrated efficacy under visible light irradiation. Light-expanded clay aggregate beads were chosen as the substrate for photocatalyst immobilization, which enhanced the reaction rate. The stability of CuO/TiO2 was evidenced by about 2% reduction in efficiency after four degradation cycles.

A physical simulation-machine learning model for optimal process schemes in laser-based directed energy deposition process

The major challenge faced is the definition of optimal process variables for rich quality of fabricated parts in laser-based directed energy deposition (DED-LB) processes. However, predicting the optimal process scheme using machine learning models is still challenging owing to the need for a large amount of training experimental data with high costs in DED-LB. In view of this, a physical simulation (PS)-machine learning (ML) model using a relatively small accurate data set of 31 simulation data for optimal process schemes is proposed in DED-LB process in this study. Firstly, a powder-scale high precision phenomenon model incorporating the mass transfer, phase transformations and heat transfer and using Lagrangian particle model to add mass is developed to depict the DED-LB process. Then, a Gaussian process regression (GPR) agent model is established to rapidly and accurately predict the geometry and dilution rate of deposition tracks under different manufacturing parameters based on the high precision simulation results. Finally, a PS-ML model for process parameter optimization is developed using the particle swarm algorithm (PSO), and the optimized parameters are experimentally validated. The results show that the developed powder-scale model and GPR model results are consistent with the Inconel 718 alloy experimental results. The proposed PS-ML model can increase the accuracy of the ML models even with a small simulation data set. None of PS-ML model optimization results has a relative error of more than 3% to the experimental results, and the dilution rate is reduced by up to 61.66% compared to the experimental design parameters without optimization. The proposed physical simulation-machine learning model in this study enables inexpensive and accurate optimization of DED-LB process parameters.

Surface morphology of API 5L X65 pipeline steel processed by ultrasonic impact peening: An integrated experimental and computational study

Ultrasonic impact peening (UIP) is a highly effective surface engineering technique to improve material durability through peening-induced surface plastic deformation. These improvements are determined by not only the generation of surface compressive residual stresses but also the alteration of surface morphological features. In literature, researchers have focused primarily on the average roughness (Sa) to understand the morphology changes induced by UIP. From the surface engineering perspective, in addition to Sa, other surface morphological characteristics play key roles in determining material durability, particularly surface skewness (Ssk) and surface kurtosis (Sku), which describe the asymmetrical distribution of surface profiles and the sharpness of peak features, respectively. In this study, surface morphological characteristics of API 5L X65 pipeline steel processed by UIP are investigated with a focus on the effects of processing parameters on Sa, Ssk, and Sku. The results indicate that the optimized UIP conditions lead to the preparation of surfaces with valley structures as the dominant feature accompanied by blunt peaks, resulting in the elimination of stress concentration sites towards enhanced material durability. In addition, surface hardness, residual stresses, and peening-induced deformation depth are measured. Furthermore, to gain a comprehensive understanding of the material response and the underlying mechanisms driving surface morphological alterations by UIP, a finite element method (FEM)-based model is developed. By investigating the effect of process parameters (peening amplitude, peening cycles, and pin size) on the surface deformation depth and stress/strain distribution, a correlation between these process variables with surface characteristics is established.

Two-dimensional LSTM soft sensor using noisy process data

Establishing a reliable data-based soft sensor still faces a series of challenges, particularly the presence of outliers and different kinds of noise which are non-negligible in process data. To address these challenges, a correntropy-based two-dimensional long short-term memory (TLSTM) model is developed to handle noisy process data. First, the multidimensional time series samples are reconstructed into numerous two-dimensional input matrices in the feature and time directions. Then, the convolution and pooling operations are used to extract useful information in the process variables related to the quality variable. Meanwhile, a gating mechanism is employed to learn the internal representation of time series. Finally, a correntropy-based strategy is utilized to assign relatively small weights to outliers automatically, enabling reliable prediction. Two cases illustrate the reliability and advantages of TLSTM in effectively extracting quality-related features for prediction.

Endophytic fungi-assisted biomass synthesis of eco-friendly formulated silver nanoparticles for enhanced antibacterial, antioxidant, and antidiabetic activities

Fungi-mediated biosynthesis of silver nanoparticles is one of the promising nanomanufacturing processes. Using endophytic fungi isolated from potential medicinal plants has emerged as a novel approach for producing several metal oxide nanoparticles in a green and cost-effective manner. The present study validates the biomimetic synthesis of silver nanoparticles of endophytic fungus (Phyllosticta capitalensis Henn.) (Phyllo-AgNPs) isolated from the leaves of Annona muricata and the effect of process variables, i.e., reductant concentration, ratio of AgNO3 to reductant concentration, reaction time and reaction pH to achieve maximum efficiency using the factorial design approach. The nanoparticle formation was confirmed by absorption peaks between 400 nm and 500 nm, followed by their characterization by UV–visible spectroscopy, TEM, SEM-EDX, XRD, particle size, and zeta potential analysis. The in-vitro antioxidant, antidiabetic and antimicrobial potential (agar well dfiffusion and minimum inhibitory concentration) of Phyllo-AgNPs at various concentrations were assessed. The Phyllo-AgNPs scavenged the 2,2-diphenyl-1-picrylhydrazyl radical and inhibited the activity of α-amylase and α-glucosidase enzymes at an IC50 of 31.75 ± 0.70, 39.32 ± 1.12 and 28.56 ± 1.08 μg/mL, respectively. The antimicrobial potential was determined against Listeria monocytogenes, Staphylococcus aureus, Escherichia coli and, Salmonella typhimurium. The study revealed the therapeutic potential of Phyllo-AgNPs against pathogenic microorganisms. Additionally, the in-vitro antioxidant and antidiabetic activities showed improved results compared to the standard (ascorbic acid and acarbose). By taking note of this, Phyllo-AgNPs can be further studied for pre-clinical trials, which would be beneficial in developing novel medicines with no adverse effects for the benefit of mankind.

Improved extended empirical wavelet transform for accurate multivariate oscillation detection and characterisation in plant-wide industrial control loops

The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers’ outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT.

Identification research of chemical process leakage based on deep learning and correlation-distance graph coding

As a leak identification method in chemical processes, deep learning is limited by inadequate sample labels and highly coupled, nonlinear process variables, therefore difficult to model with a high identification accuracy. To address these problems, a fusion correlation-distance graph (CDG) coding aided deep learning method is proposed. First, the mechanism model is built based on dynamic process data with different leakage labels. Then, the weighted degree and spatial distribution of the dynamic dataset are computed through innovatively designed correlation and distance coding, aided by sliding window fusion into a correlation-distance matrix. Finally, the well-trained long short-term memory (LSTM) is constructed to learn the deep features of the leakage process and identify them. The applications in two industrial cases, CO2 capture and ammonia synthesis, show that CDG-LSTM presents state-of-the-art identification performance with R2 scores of 0.994 and 0.986. 6.6% and 18.4% improvement over LSTM. In addition, the proposed method effectively categorizes the unknown leak samples into known labels with nearby locations, providing a valuable reference for operators to determine the unknown leak locations.

Multi-objective optimization of wire electrical discharge machining process using multi-attribute decision making techniques and regression analysis

Wire electrical discharge machining (WEDM) is one of the most important non-traditional machining methods that is widely used in various industries. The present research work is concerned with the influences of process variables on quality of machined specimen obtained from WEDM process. The process parameters to manufacture mold structure included wire feed speed, wire tension and generator power, and in the current research, the effects of these variables on the aim factors, namely dimensional accuracy, hardness and roughness of product surface have been investigated, simultaneously. In order to obtain the optimal experiment, the multi-objective optimization with discrete solution area has been employed. Method based on the removal effects of criteria (MEREC) and weighted aggregates sum product assessment (WASPAS) techniques have been used with the aim of weighting the objective functions and discovering the best practical experiment. In the following, the regression analysis has been employed to study the effects of variables on response factors. A good correlation between the results gained from two analysis methods was observed. Based on MEREC-WASPAS hybrid technique, the weights of roughness, hardness and dimensional accuracy of machined part were calculated to about 89%, 9% and 2%, respectively. In the selected optimal experiment, the amount of wire feed speed, wire tension and generator power variables were considered to, in turn, 2 cm/s, 2.5 kg, and 10%.

In Situ Changes in Mechanical Properties Based on Gas Saturation Inside Pressure Vessels.

In previous studies, difficulties were encountered in measuring changes within high-pressure vessels owing to limitations such as sensor connectors and sensor failures under high-pressure conditions. In addition, polymer-gas mixtures experience instantaneous gas desorption upon exiting high-pressure vessels owing to pressure differentials, leading to measurement errors. In this study, a device using magnetic sensors was developed to measure the real-time changes in gas-saturated polymers inside pressure vessels. Experiments on polymethyl methacrylate gas adsorption were conducted with parameters including pressure at 5 MPa and temperatures ranging from -20 to 40 °C for 60 and 180 min. It was observed that at -20 °C, the maximum magnetic field force density and deflection were 391.53 μT and 5.83 mm, respectively, whereas at 40 °C, deflection did not occur, with a value of 321.79 μT. Based on gas saturation experiments, a new model for deflection in high-pressure atmospheres is proposed. Additionally, an ANSYS analysis was conducted to predict the changes in Young's modulus based on gas saturation. In previous studies, mechanical properties were measured outside the pressure vessel, resulting in an error due to a pressure difference, while the proposed method is characterized by the ability to directly measure polymer behavior according to gas saturation in high-pressure vessels using a magnetic sensor in real time. Therefore, it is possible to predict polymer behavior, making it easy to control variables in high-pressure polymer processes.

Deep ensemble learning for material removal rate prediction in chemical mechanical planarization with pad surface topography

Chemical mechanical planarization (CMP) stands as a critical process in semiconductor manufacturing, necessitating precise prediction of material removal rate (MRR) to achieve optimal global planarization of material surface step heights. A notable hurdle in MRR prediction lies in the nonlinear pad-wafer interaction stemming from variations in the polishing pad surface. Despite numerous extant studies examining the asperity of the pad surface and the wafer-pad contact, aspects such as slurry lubrication and micro-topographical structure of the pad surface remain unaddressed. This study firstly proposes a deep ensemble learning-based approach for MRR prediction, leveraging the intricate interactions between the overall features of the pad surface and the polishing pressure. We experimentally generated diverse pad surfaces by controlling the process variables governing pad surface topography. The Multi-TabNet model, constructed by independently training multiple TabNet instances with distinct initialization and hyperparameters, furnishes a framework for MRR prediction across a broad spectrum of pad surface features, encompassing pores and cores. The results demonstrate a root mean square error of 80 Å/min, with the error margin reduced by up to 70 % compared to predictions utilizing a single parameter of the pad surface. Furthermore, it exhibits an error improvement exceeding 17 % relative to the existing method solely considering asperity. This study not only imparts insights for addressing MRR variations contingent on pad condition but also facilitates dependable material removal even on variable pad surfaces due to conditioner wear.

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Purpose Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters. Design/methodology/approach In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model. Findings The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value. Originality/value By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

Research on Predicting Welding Deformation in Automated Laser Welding Processes with an Enhanced DEWOA-BP Algorithm

Research on Predicting Welding Deformation in Automated Laser Welding Processes with an Enhanced DEWOA-BP Algorithm

Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning

Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially correlated and contaminated by measurement uncertainty. A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation. RDVDL employs a sparse Bayesian dictionary learning to preserve the dynamic mechanism information of AWE process which allows the easy interpretation of fault detection results. To improve the robustness to measurement uncertainty, a low-rank vector autoregressive (VAR) method is derived to reliably extract the serial correlation from process variables. The effectiveness of the proposed approach is demonstrated with an industrial hydrogen production process, and RDVDL can efficiently detect and diagnose critical AWE faults.

Extraction of bioactive compounds from Vitex megapotamica: A study and selection of variables

AbstractFruits are widely used to obtain and study bioactive compounds. Among them, the tarumã (Vitex megapotamica) is still understudied. The extraction of bioactive compounds from the powdered pulp of the ripe fruit of the tarumã was studied from the selection of several variables of the extraction process through the experimental design Plackett–Burman: the pH, the solid:liquid ratio, agitation, and temperature in an extraction time of 3 h. The extracts obtained were evaluated for antioxidant activity by 2,2′‐azino‐bis‐(3‐ethylbenzothiazoline‐6‐sulfonic acid) (ABTS), 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH), and for total phenolic compounds, as well as the presence of flavonoids, anthocyanins, carotenoids, and identification of bioactive compounds was assessed by high‐performance liquid chromatography (HPLC). Using the experimental design, the best extraction condition was defined with a pH of 5, solid:liquid ratio of 1:30, stirring at 100 rpm, with a temperature of 80°C, obtaining 104.21 mg Gallic Acid Equivalent (GAE) 100 g−1 for phenolic compounds total, 742.71 μmol Trolox g−1 for ABTS, and EC50 of 2884.98 mg L−1 for DPPH. From HPLC, the presence of fumaric acid, gallic acid, p‐coumaric acid, and caffeic acid in the extracts was detected. Regarding flavonoids, anthocyanins, and carotenoids, the values obtained were 6.38 mg ECG g−1, 1.67 mg ECG g−1, and 2.89 mg 100 g−1, respectively. The selection of extraction conditions allowed obtaining better levels of bioactive compounds in the powdered pulp of tarumã extract, even with the use of water as a solvent, to avoid the presence of chemical residues in the extract, with antioxidant potential and viability for application in food products.Practical applicationsRecent research suggests a change in perspective regarding the consumption of healthier foods from plants and fruits that have important health properties. In this regard, tarumã (Vitex megapotamica) fruit has been standing out as a potential source of bioactive compounds with significant medicinal properties. The extracts obtained have a significant amount of phenolic compounds, as well as the presence of flavonoids, anthocyanins, and carotenoids, in which all of these have great potential bioactive activities. Thus, a good methodology of extraction and characterization of tarumã extract becomes a crucial part of any food, chemical, or pharmaceutical industry sector. In this regard, the present study with experimental design Plackett–Burman extraction is the way ahead due to high bioactive retention and the cost implications of the process.

An experimental investigation and predictive modeling using machine learning technique for reclamation of metal values from scrap NdFeB magnets

Neodymium-Iron-Boron (NdFeB) magnets are widely used in various industries due to their exceptional magnetic properties, such as high coercivity, remanence, and maximum energy product. These magnets consist of rare earth elements (REEs) viz., neodymium (Nd), praseodymium (Pr), and Dysprosium (Dy), along with other metals. With the ever increasing demand for REEs and the need to bridge the supply gap, it is crucial to develop alternative methods for their extraction. Recycling metal from scrap magnets is a promising approach to address the pressure on the supply chain and work towards sustainable development. The primary goal of this study is to generate a predictive model based on machine learning to determine the optimal conditions for metal recovery from scrap NdFeB magnets through water leaching after chloridizing roasting. Bench-scale leaching experiments were carried out to generate a dataset for statistical process optimization and machine learning analysis. The leaching kinetics of neodymium was also explored, and mixed-controlled shrinking core model was found to be most suitable, with an activation energy of 58.11 kJ/mol in the temperature range of 25– 95 °C. This study is the first to utilize a machine learning approach to analyze the potential process variables and their impact on metal recovery from calcined NdFeB magnet powder. A comparative analysis between experimental and machine learning approaches is presented to predict the optimal conditions for selective recovery of metal values from scrap NdFeB magnets. The maximum efficiency of extraction of metal ions was found to occur at a temperature of 95 °C, solid to liquid ratio of 125 g/l, and leaching duration of 60 min.

Microwave heating concentration of raspberry pulp: Evaluation of processing variables on concentration characteristics and quality attributes

Concentration of fruit pulp is an important unit operation in food processing and has a wide range of applications. In this study, the microwave heating concentration (MHC) of raspberry pulp at different microwave powers, heating times and sample masses were investigated considering concentration characteristics and quality attributes. The results showed that increasing microwave power/heating time or decreasing sample mass significantly decreased the moisture content but had no significant effect on the temperature of raspberry pulp, while these conditions resulted in loss of total anthocyanin content and deterioration of total color difference. LF-NMR and SEM results revealed that the changes in temperature and moisture content caused by MHC significantly affected the total anthocyanin content and total color difference of the final product. Microwave power of 800 W, heating time of 3 min and sample mass of 90 g are selected as suitable parameters for MHC of raspberry pulp. This study may provide guidance for the development of appropriate technology for MHC of berry pulp.

Flash sintering of metal-like ceramics: A short overview

Flash sintering of metal-like ceramics: A short overview

Transesterification of Persea americana seed oil to methyl ester using bio-based heterogeneous catalyst: Optimization and techno-economic analysis

The techno-economic analysis of producing biodiesel from agrarian wastes at optimal condition was carried out. The acid activated empty palm fruit bunch ash (AAEPFBA) catalyzed the methyl ester production. The AAEPFBA catalyst was synthesized from waste palm fruit bunch, and modified with phosphoric acid. The activation was best achieved by adding H3PO4 acid in a ratio of 1:2 (g/ml). The modification of the catalyst increased the alkaline properties and surface area and decreased the particle size and adsorption energy. The process variables of temperature 61.9 °C, reaction time of 3.09 h, methanol/oil molar ratio of 10.2:1, catalyst concentration of 3.14wt%, and agitation speed of 321.54 rpm at a desirability of one gave an experimental optimal yield of biodiesel as 90.3%. Based on the experimental study, 3062 liters (2728kg) of biodiesel was evaluated annually. The annual operating cost, payback period, and internal rate of return are ₦2201288, 1.5years, and 7.6%, respectively. The sensitivity analysis of oil purchase cost, biodiesel selling price, and labour cost on Return on investment, PBP, IRR, and Net present value were investigated. In this study, the relative biodiesel selling price is a determinant of the profitability of producing avocado pear seed oil methyl ester. The overall results show that investors can embark on biodiesel production using avocado pear seed oil, and AAEPFBA as feedstocks.

Studies on Encapsulation of Gas Releasing Agent in Ca-Alginate Beads and Controlled Release Pattern of the Beads

Calcium alginate beads have been used to control release gases and volatile compounds for agricultural applications, drug delivery, waste treatment, and food product enhancement. The release rate of the gas and vapor depends on the diffusion of gas and vapor through the Ca-alginate gel matrix. However, the knowledge about the effect of process variables on the diffusion rate of gas and vapor still needs to be improved. Therefore, this study aims to investigate the effect of alginate concentration, gas release agent concentration, and bead size on the concentration of gas released from Ca-alginate beads. In this study, calcium carbonate was used as the model gas release agent, encapsulated in Ca-alginate beads to release carbon dioxide when the beads reacted with an acetic acid solution. The results showed that the concentration of carbon dioxide released by the beads depended on the bead diameter. The gas release rate was increased when the diameter of the beads was increased. The total amount of gas produced by the large beads was observed to be higher than that of small beads. Compared to small beads, large beads take longer to react completely to produce carbon dioxide gas. It was also found that the amount of carbon dioxide produced by the Ca-alginate beads was inversely proportional to alginate concentration but directly proportional to calcium carbonate concentration. The results indicated that the gas diffusion rate of the Ca-alginate beads can be tailored by adjusting the process variables of the Ca-alginate bead encapsulation process.