Process Optimization Approach Research Articles

Evaluation of different approach for WEDM process optimization

In this paper, an effective process optimization approach based on artificial neural networks with a backpropagation and response surface methodology including central composite design is presented for the modeling and prediction of material removal rate in the wire electrical discharge machining process. In the development of predictive models, cutting parameters of pulse on time, pulse off time, peak current, spark gap set voltage, wire tension, and wire feed rate are considered as model variables. After experiments are carried out, the BPNN model was developed using six process parameters. The performance of the developed artificial neural networks and response surface methodology predictive models is tested for prediction accuracy in terms of the coefficient of determination and root mean square error metrics. MRR's CCD statistical models were compared to the performance of the created BPNN models. On 52 investigative data points, the training of a neural network model based on back-propagating was evaluated. The results indicate that an artificial neural networks model provides a more accurate prediction than the response surface methodology model.

Dissimilar tungsten inert gas welding between Inconel 718 and commercially pure titanium using a vanadium interlayer

A weight reduction of aero engines, in order to enhance their efficiency would be possible if the commercially pure titanium in the low-temperature region of the compressor could be welded with Inconel in the high-temperature portion. This joining of titanium/Inconel is challenging owing to the formation of hard TixNiy intermetallic compounds, the suppression of which is not possible using the conventional weld process optimization approach. In recent years, a number of approaches have been developed to reduce or eliminate these intermetallic compounds during welding and one approach is the use of an interlayer during the welding operation. The insertion of a V interlayer at the root side remarkably suppressed Ti and Ni diffusion across the interlayer. NiV3 and (Ti, V) solid solutions were present in the interfacial microstructure of V/Inconel 718 and V/commercially pure titanium, respectively, as characterized by scanning electron microscope and X-ray diffraction. The tensile strength of the weldment was 190 MPa (approx. 59% of the commercially pure titanium base metal) with an elastic modulus comparable with that of the base alloys. The joint exhibited brittle fracture at the Inconel 718 side near the V/Inconel 718 interface due to intermetallic compounds.

An Innovative Agile Model of Smart Lean–Green Approach for Sustainability Enhancement in Industry 4.0

Industry 4.0 emphasizes developing an innovative approach to eliminating the problems caused by environmental and shop floor waste, which is accomplished by a suitable process optimization approach. The process optimization approach is used to maximize productivity within limited constraints by observing end-to-end management systems. The present research work developed an innovative agile model using the lean, smart, and green approach to improve operational performance within limited constraints in Industry 4.0. The proposed model was developed by thoroughly reviewing research articles conducted over the past decades on process optimization approaches that include lean manufacturing, smart manufacturing, kaizen, and lean six sigma. The model was validated through two real production case studies in the mining machinery and automobile industries. The present article concluded that overall operational performance was enhanced in both case studies by improvement in different factors, including working environment, worker efficiency, environmental evolution, logistics management, and resources utilization. The authors of the present article strongly believe that the proposed innovative agile model would help people in industry make aesthetic and smart sustainable production systems in Industry 4.0 within limited constraints.

Optical properties of large-size and damage-free polished Lu2O3 single crystal covering the ultraviolet-visible-and near-infrared (UV–VIS–NIR) spectral region

Lu2O3 has attracted widespread attention in high-power lasers, scintillators, and semiconductors due to its high thermal conductivity, high stability, and large band gap. Due to the limitations of the growth process, high-precision characterization of polished quality and optical properties for large-size Lu2O3 single crystal is extremely scarce, which hinders the design and optimization of optics based on Lu2O3. In this study, we design a systematic machining scheme for large-size Lu2O3 single crystal thick chip to obtain polished crystal with virtually no surface and subsurface damage. The surface/subsurface quality was evaluated utilizing optical profilometer, scanning electron microscope, and transmission electron microscope. Variable-angle spectroscopic ellipsometer was employed to measure the optical constants over wide spectral region (210–1690 nm), removing the lack of optical constants of Lu2O3 single crystal in the ultraviolet region. The band gap is analyzed based on the optical constants results. The consistency of the measured data with the relevant literature verifies the reliability and accuracy of the measurement method along with the data. The surface/subsurface damage inspection method provides an effective approach for ultra-precision machining mechanism research and process optimization of Lu2O3 and other hard and brittle materials. The band gap and the optical constants in wide spectral region provide critical fundamental data for the design and performance optimization of related optics.

Incorporating the occupational health in the optimization for the methanol process

The analysis of social aspects, such as occupational health, is as important as economic and environmental criteria in the design of sustainable chemical processes. Occupational health analyzes the relationship between work and health while enabling the identification of hazards that can cause injuries due to chronic exposure of the workers. This paper presents a multi-objective optimization approach for the methanol production process from natural/shale gas, different alternatives are proposed for the process configurations based on different equipment and technologies. The objective functions consist of maximizing the net profit, minimizing the total annual CO2 emissions, and minimizing the occupational health hazard. The Process Route Healthiness Index is used to evaluate occupational health. The process model is developed using the Aspen HYSYS® process simulator. To carry out the optimization, a metaheuristic algorithm is used; the improved multi-objective differential evolution (I-MODE) algorithm was used with programming in Microsoft Excel visual basic for applications. Linking routines are established to enable the transfer of values of the process variables. A set of decision variables are established in a search interval whose manipulation has a considerable impact on the performance of the objective functions. The results offer feasible alternatives for the values of the search variables and show a considerable improvement in the specified objective functions. Different scenarios are proposed to prioritize each of the objectives for which the decision-maker can make a selection of the best values and the configuration that best suits specific interests.

Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network

Grit blasting as a pretreatment process for the substrate surface before thermal spraying is of great importance for assuring the service performance of thermal spraying coatings. In this work, a novel hybrid artificial neural network (ANN) was presented to optimize the grit blasting process to improve the structural properties and corrosion resistance performance of thermal spraying coatings. Different grit blasting process parameters were combined to pretreat the substrate surface, and the corresponding surface roughness, interface adhesion strength and corrosion resistance performance were obtained. Hence, a backpropagation (BP) neural network model optimized by the genetic algorithm (GA) was presented to address the poor regression roughness and accuracy of the traditional fitting models; the grit blasting processing parameters were utilized as the inputs for the GA–BP model; the structural properties and the corrosion resistance performance were used as the outputs. The correlation coefficient R reached and exceeded 0.90, and three error values were less than 1.75 on the prediction of the service performance of random samples. All these indicators demonstrated convincingly that the obtained hybrid artificial neural network models possessed good prediction performance, and this innovative and time-saving grit blasting process optimization approach could be potentially employed to improve the comprehensive service performance of thermal spraying coatings.

Mechanistic modeling of continuous capture step purification of biosimilar monoclonal antibody therapeutic

AbstractBACKGROUNDContinuous multicolumn Protein A chromatography offers various advantages for capture stage purification of monoclonal antibody therapeutics, like higher productivity and resin capacity utilization, lower buffer consumption, small footprint, etc. Due to the complexity of the continuous process, experimental optimization is time‐consuming and cost‐intensive. This investigation proposes a hybrid process development approach integrating experimental and mechanistic modeling for time‐ and cost‐effective development and optimization of continuous Protein A affinity chromatography.RESULTSProductivity and capacity utilization of the continuous CaptureSMB process under varying operating conditions were predicted using the Chromatography Analysis and Design Toolkit (CADET) framework and validated with experimental results. Effects of critical process parameters like feed concentration (c0), loading breakthrough (s) and residence time (RT) on productivity and capacity utilization were evaluated. Model predictions were validated using the experimental results proving the reliability and feasibility of the modeling approach. At 15.00 ± 0.20 mg mL–1 feed model mAb concentration, the model‐based approach predicted the best performance giving 27.56 g L–1 h–1 productivity (RT = 2 min, s = 80%) using MabSelect™ PrismA resin. This productivity prediction was validated by performing the continuous CaptureSMB experiment at the same operating conditions and was found to be 25.59 g L–1 h–1 with a root mean square error of 1.96.CONCLUSIONSThe hybrid process development and optimization approach for CaptureSMB purification of biosimilar mAb purification is demonstrated and validated using the experimental and model‐based framework. The observed results showcase the effectiveness of the proposed hybrid process development approach, enabling a better understanding of the chromatography process and parameters affecting the CaptureSMB process. © 2021 Society of Chemical Industry (SCI).

A printability assessment framework for fabricating low variability nickel-niobium parts using laser powder bed fusion additive manufacturing

PurposeThere is recent emphasis on designing new materials and alloys specifically for metal additive manufacturing (AM) processes, in contrast to AM of existing alloys that were developed for other traditional manufacturing methods involving considerably different physics. Process optimization to determine processing recipes for newly developed materials is expensive and time-consuming. The purpose of the current work is to use a systematic printability assessment framework developed by the co-authors to determine windows of processing parameters to print defect-free parts from a binary nickel-niobium alloy (NiNb5) using laser powder bed fusion (LPBF) metal AM.Design/methodology/approachThe printability assessment framework integrates analytical thermal modeling, uncertainty quantification and experimental characterization to determine processing windows for NiNb5 in an accelerated fashion. Test coupons and mechanical test samples were fabricated on a ProX 200 commercial LPBF system. A series of density, microstructure and mechanical property characterization was conducted to validate the proposed framework.FindingsNear fully-dense parts with more than 99% density were successfully printed using the proposed framework. Furthermore, the mechanical properties of as-printed parts showed low variability, good tensile strength of up to 662 MPa and tensile ductility 51% higher than what has been reported in the literature.Originality/valueAlthough many literature studies investigate process optimization for metal AM, there is a lack of a systematic printability assessment framework to determine manufacturing process parameters for newly designed AM materials in an accelerated fashion. Moreover, the majority of existing process optimization approaches involve either time- and cost-intensive experimental campaigns or require the use of proprietary computational materials codes. Through the use of a readily accessible analytical thermal model coupled with statistical calibration and uncertainty quantification techniques, the proposed framework achieves both efficiency and accessibility to the user. Furthermore, this study demonstrates that following this framework results in printed parts with low degrees of variability in their mechanical properties.

Hydrogen production via integrated configuration of steam gasification process of biomass and water‐gas shift reaction: Process simulation and optimization

A comprehensive model is developed to predict and optimize the hydrogen production via integrated configuration of steam gasification process of biomass and water-gas shift reaction by taking advantage of the ASPEN plus software and sensitivity analysis techniques. The steam gasification process of three different generations of biomass, including corn cob as the first generation, wood residue and rice husk as second generation, and Spirulina algae as the third, are investigated and evaluated to achieve maximum hydrogen production. This model is successfully validated with experimental data reported in the literature about steam gasification of rice husk in the fluidized bed reactor. The impact of main operating parameters is considered in terms of products composition, hydrogen yield, CO conversion, H2/CO ratio in the gas products stream, and cold gas efficiency (CGE). The results indicated that the maximum hydrogen concentration is achieved at the highest steam to biomass (S/B) ratio in the gasifier and water-gas shift (WGS) reactor and at the lowest WGS reaction temperature, whereas there is an optimum value about the gasification temperature. The highest CO conversion and H2/CO molar ratio belong to rice husk biomass at all considered range of temperature, whereas they are almost the same for wood residue and Spirulina. The predicted results confirmed that CGE of all feedstocks improves with increasing gasification temperatures and S/B ratio. The steam gasification performance of different feedstocks at 750°C is ranked as wood residue (83.56%) > spirulina (83.47%) > corn cob (74.94%) > rice husk (69.86%). The presented configuration can be applied as a novel approach for process evaluation and optimization through the downdraft biomass gasification integrated with water-gas shift reaction to intensify the hydrogen production.

Vision based quality control of composite components: considerations about measurement accuracy

In this paper a methodology is discussed concerning the measurement of yarn’s angle of a reinforced polypropylene matrix used in the production of automotive components. The measurement method is based on a vision system and advanced processing of images in order to evaluate the geometrical parameters of interest; the accuracy of measurement is a mandatory requirement, in order to assess the simulation approach for thermoplastic process optimization. Many aspects influencing the whole accuracy of the method have been identified and their effect evaluated, of both geometrical and optical type, allowing to perform angle measurements of the fiber angle with a whole accuracy in the order of a few degrees. By this way both local and extended defects can be identified in a reliable way also with reference to components of complex geometry. According to these results, accurate measurements of angle allows us to both validate the simulation of the thermoplastic process and to give suggestions for process improvement of fiber glass components of complex geometry.

Development of FEA-ANN-Integrated Approach for Process Optimization of Coaxial One-Side Resistance Spot Welding of Al5052 and CFRP

Abstract Coaxial one-side resistance spot welding (COS-RSW) is a newly developed process for joining metals and composites. In the present study, Al5052 and carbon-fiber-reinforced plastic (CFRP) lap joints were fabricated via COS-RSW. The welding process was modeled numerically using an in-house finite element code called JWRIAN. Single-lap shear tests were performed to evaluate the joining strength. The molten zone diameter was defined and measured experimentally to verify the numerical model. An artificial neural network (ANN) was established based on multitask learning, and its training data set was prepared via finite element analysis (FEA). The well-trained ANN was employed to generate a process window for the COS-RSW. Results demonstrated that the FEA could accurately reproduce the COS-RSW process, which served as an efficient tool for generating a process data set without performing experiments. The ANN performed multitask learning well and predicted the welding output effectively. Furthermore, Tmavg, an index representing the average value of the maximum temperature in the molten interface of CFRP, was adopted to evaluate the contribution of the integral interface temperature field to the bonding strength qualitatively. An optimal Tmavg value, which was close to the CFRP decomposition temperature of 340 °C, was obtained, and it exhibited an excellent correlation with higher bonding strengths. The process window provided welding parameters directly to yield the desired results.

Use of Bead Mixtures as a Novel Process Optimization Approach to Nanomilling of Drug Suspensions.

We aimed to evaluate the feasibility of cross-linked polystyrene (CPS)-yttrium-stabilized zirconia (YSZ) bead mixtures as a novel optimization approach for fast, effective production of drug nanosuspensions during wet stirred media milling (WSMM). Aqueous suspensions of 10% fenofibrate (FNB, drug), 7.5% HPC-L, and 0.05% SDS were wet-milled at 3000-4000rpm and 35%-50% volumetric loading of CPS:YSZ bead mixtures (CPS:YSZ 0:1-1:0 v:v). Laser diffraction, SEM, viscometry, DSC, and XRPD were used for characterization. An nth-order model described the breakage kinetics, while a microhydrodynamic model allowed us to gain insights into the impact of bead materials. CPS beads achieved the lowest specific power consumption, whereas YSZ beads led to the fastest breakage. Breakage followed second-order kinetics. Optimum conditions were identified as 3000rpm and 50% loading of 0.5:0.5v/v CPS:YSZ mixture from energy-cycle time-heat dissipation perspectives. The microhydrodynamic model suggests that YSZ beads experienced more energetic/forceful collisions with smaller contact area as compared with CPS beads owing to the higher density-elastic modulus of the former. We demonstrated the feasibility of CPS-YSZ bead mixtures and rationalized its optimal use in WSMM through their modulation of breakage kinetics, energy utilization, and heat dissipation.

Reviews on Machine Learning Approaches for Process Optimization in Noncontact Direct Ink Writing.

Recently, machine learning has gained considerable attention in noncontact direct ink writing because of its novel process modeling and optimization techniques. Unlike conventional fabrication approaches, noncontact direct ink writing is an emerging 3D printing technology for directly fabricating low-cost and customized device applications. Despite possessing many advantages, the achieved electrical performance of produced microelectronics is still limited by the printing quality of the noncontact ink writing process. Therefore, there has been increasing interest in the machine learning for process optimization in the noncontact direct ink writing. Compared with traditional approaches, despite machine learning-based strategies having great potential for efficient process optimization, they are still limited to optimize a specific aspect of the printing process in the noncontact direct ink writing. Therefore, a systematic process optimization approach that integrates the advantages of state-of-the-art machine learning techniques is in demand to fully optimize the overall printing quality. In this paper, we systematically discuss the printing principles, key influencing factors, and main limitations of the noncontact direct ink writing technologies based on inkjet printing (IJP) and aerosol jet printing (AJP). The requirements for process optimization of the noncontact direct ink writing are classified into four main aspects. Then, traditional methods and the state-of-the-art machine learning-based strategies adopted in IJP and AJP for process optimization are reviewed and compared with pros and cons. Finally, to further develop a systematic machine learning approach for the process optimization, we highlight the major limitations, challenges, and future directions of the current machine learning applications.

Comparison of ANN and RSM modeling approaches for WEDM process optimization

Abstract In this paper, an effective process optimization approach based on artificial neural networks with a back propagation algorithm and response surface methodology including central composite design is presented for the modeling and prediction of surface roughness in the wire electrical discharge machining process. In the development of predictive models, cutting parameters of pulse duration, open circuit voltage, wire speed and dielectric flushing are considered as model variables. After experiments are carried out, the analysis of variance is implemented to identify the contribution of uncontrollable process parameters effecting surface roughness. Then, a comparative analysis of the proposed approaches is carried out to determine the most efficient one. The performance of the developed artificial neural networks and response surface methodology predictive models is tested for prediction accuracy in terms of the coefficient of determination and root mean square error metrics. The results indicate that an artificial neural networks model provides more accurate prediction than the response surface methodology model.

Application of supercritical fluid extraction for extraction or enrichment of phospholipids in egg and dairy products: A review

Phospholipids (PLs) are polar lipids that are found in dairy products, egg, and oilseeds. They are amphiphilic in nature and find application in many foods as a functional ingredient. As PLs are distributed in food matrix in dilute form, attempts to concentrate the same by adopting suitable technical interventions have been widely investigated. One of the more successful approaches that have been reported is the application of supercritical fluid extraction (SCFE) to extract PLs using suitable cosolvents. The extraction protocol to extract PLs from dairy products and eggs using SCFE also includes a two‐step process, namely, separation of neutral lipids using neat CO2 followed by subsequent separation of polar lipids from the residue using cosolvent and CO2. This review is an attempt to compile information on dairy and egg PLs extracted using SCFE under varied operational factors. It is expected that the compilation will serve as resource for investigators working in this research area.Practical ApplicationsMilk fat processing results in valuable by‐products which can be suitably volarized to increase profitability through processing gains and reduce environmental footprints in dairy industry. The by‐products such as buttermilk, butter serum, ghee residue, and so forth can be tapped as potential sources for phospholipid (PL). Eggs have already been recognized as a good source of PLs, which are reported to have preventive effect on pathogens, cancer protective, anti‐inflammation and cardiovascular disease. Two challenges could be anticipated in the fractionation of PLs from egg and milk by‐products. First, extraction of lipids from substrate which include polar and neutral lipids. Second, separation of neutral and polar lipids from the lipid extract. Conventional methods of extraction involve many solvents for the wide range of lipids to be extracted, which often result in incomplete extraction. Supercritical fluid extraction (SCFE) technique is reported to be useful in extraction and separation of PLs from dairy and egg products. Process intervention strategies and optimization approaches have been undertaken by various researchers to quantify and improve extractability of PLs using SCFE. The paper is a compilation of the scientific information related to extraction of dairy and egg PLs using SCFE, which will serve as a knowledge resource for researchers working in related fields.

A Hybrid Genetic Programming–Gray Wolf Optimizer Approach for Process Optimization of Biodiesel Production

Biodiesel is one the most sought after alternate fuels in the current global need for sustainable and renewable energy sources due to their lower emissions and no major modification requirement to existing engines. However, the performance and productivity of the biodiesel production process are significantly dependent on the process parameters. In this regard, a novel hybrid genetic programming-gray wolf optimizer approach for the process optimization of biodiesel production is proposed in this paper. For an illustration of the proposed approach, kinematic viscosity is expressed as a symbolic regression metamodel to account for the influence of catalyst concentration, reaction temperature, alcohol-to-oil molar ratio, and reaction time. Then, the genetic programming-based symbolic regression metamodel is used as an objective function by the gray wolf optimizer to optimize the process parameters. The obtained results show that the proposed approach is simple, accurate, and robust.

Optimization Approach for Solvent Extraction Process of Oily Contaminated Soil with Addition of Biosurfactant

Objectives : Solvent extraction is a process in which not only enable to reduce oil contaminant from soil residue, but also capable to recover oil from soil matrix of oily contaminated soil which has opportunity to be reutilized. Optimization process has been simulated by previous studies related to type and dosage of solvents, variances of temperature, additional of surfactants, and other related parameters to increase oil removal from oily contaminated soil. This study seeks an approach of optimization for solvent extraction process to oily contaminated solid waste by conducting statistical analysis into laboratory experimentation from perspective of Total Petroleum Hydrocarbon (TPH) removal.Method : Biosurfactant became single extractors for multistage extraction process and also combined with other solvents which are acetone and toluene. Mixing method that utilized during the study was combination between horizontal shaking at 150 rpm in 15 min duration and centrifugation force at 1,570 g in 10 min duration. Statistical analysis were conducted to seek its multiple regression.Result : Study describing biosurfactant performance single extractor by using multistage extraction process achieve 77% TPH removal, while combination of biosurfactant and solvent extraction by using toluene and acetone also capable to increase TPH removal 7% higher from original performance of both toluene and acetone at solvent extraction.Conclusion : Surfactant and solvents combination is promising to improve TPH removal, while statistics analysis that implemented to observed extraction process has possibility to be used for engineering higher efficiency of extraction process.

Enhancing the biodegradation efficiency of a emergent refractory water pollutant by a bacterial isolate through a statistical process optimization approach

Clofibric acid, the main pharmacologically-active metabolite of pharmaceutical products used as antilipaemic agent has received, in last years, an increased interest because of its well-known recalcitrance to biodegradation and its high persistence in the aquatic environment. This molecule passes unchanged or poorly transformed in wastewater treatment plants. An indigenous strain of Streptomyces, named MIUG 4.89 was previously selected, exhibited the ability to favor clofibric acid biodegradation within submerged cultivation in controlled biotechnological conditions. Thus, in order to enhance the biodegradation of this refractory molecule, mathematical modeling and statistical optimization designs associated to Plackett-Burman Design and Response Surface Methodology were used to evaluate and optimize the effects of different major culture conditions of this bacterial isolate. According to the results, under optimized culture conditions (5 g L−1 glucose, inoculation level 4.7 %, 27.5 °C and 20 days of incubation) the strain Streptomyces MIUG 4.89 exhibited a successful removal of clofibric acid with a biodegradation yield of 54 %, which is in agreement with model prediction. Thus, under optimized conditions, the removal yield was enhanced, which is very promising accounting for the refractory character of this water pollutant.

Hybridized multi-objective optimization approach (HMODE) for lysine fed-batch fermentation process

A new hybrid multi-objective differential evolution (MODE) algorithm is proposed that combines the MODE algorithm for the global space search with a dynamical local search (DLS) method for the local space search. HMODE-DLS algorithm was validated using the tri-objective DTLZ7 test problem and the results were compared with MODE algorithm with respect to four performance metrics. In addition to HMODE-DLS, another three algorithms were used to solve two multi-objective optimization cases in an industrial lysine bioreactor at different feeding conditions. Case 1 considers maximizing lysine’s productivity and yield. While case 2 studies the maximization of productivity along with minimization of total operating time. In all cases, theoretical and industrial, HMODE-DLS showed a better performance with a better quality Pareto set of solutions. The Pareto front of case 1 found by HMODE-DLS was compared with a recent study trade-off, and the current non-dominated solutions values were found to be improved. This indicates that the lysine production process is enhanced. For case 2, the switching time from fed-batch to batch was found to be the key decision variable. Generally, these findings indicate the effectiveness of HMODE-DLS for the studied cases and its potential in solving real world complex problems.

BSTS Synthesis Guided by CALPHAD Approach for Phase Equilibria and Process Optimization

BSTS Synthesis Guided by CALPHAD Approach for Phase Equilibria and Process Optimization