Process Variables For Production Research Articles

Application of central composite design to minimize product variability in iron sheet production process

Application of central composite design to minimize product variability in iron sheet production process

PENGARUH PROSES PRODUKSI DAN PENGENDALIAN KUALITAS TERHADAP KUALITAS PRODUK YHA DI PT. FURUKAWA AUTOMOTIVE SYSTEM INDONESIA CIKARANG

The quality of a product is one of the most important factors in the industrial world as in PT. Furukawa Automotive System Indonesia. The company always tries to maintain the quality of its products, but inreality things don't go smoothly. Every month there are still a large number of damaged products (NG) both in the production process and in quality control that are not optimal. There fore the purpose of this research is to find out how much influence the production process and quality control have on the quality of the product produced either partially or simultaneously. This study uses a quantitative approach with primary data as a source of data obtained from the results of the questionnaire. Data analysis methods in this study are validity test, reliability test, classic assumption test which consists of normality test, heteroscedasticity test, multicollinearity test, linearity test, hypothesis test and determination test. The results of this study indicate that (1) the production process variable has a positive and significant effect on the product quality of PT. Fasi, (2) the quality control variable has a positive and significant effect on the product quality of PT. Fasi, (3) the production process variables and quality control variables together have an influence and are significant on the product quality of PT. Fasi.

Monitoring method and application of transition process with nonstationary conditions based on stability factor partitioning and RSFA

It is common for the working conditions to change with time in actual industrial processes. However, the transition modes of complex industrial processes under different working conditions often have various degrees of dynamic nonstationarity, which makes the traditional process monitoring model based on the stationarity assumption ineffective. In this paper, a Recursive Slow Feature Analysis method based on Stability Factor Partitioning (SFP-RSFA) is proposed for fine process monitoring of transition modes under dynamic nonstationarity characteristics. First, we calculate the stability factor according to the different stationarity characteristics of the production process variables. Then, K-means clustering is carried out according to the stability factor of each variable, and the stability factor of the cluster center is mapped to the interval [0,1] as the smoothing coefficient of the exponential weighted moving average (EWMA), which is applied to each data subblock respectively to highlight the steady-state and dynamic characteristics of the monitoring data subblock. In the online monitoring stage, the monitored data are fed into the subblock recursive slow feature analysis (RSFA) monitoring model. Finally, a comprehensive statistic method is proposed to integrate the subblock monitoring statistics. The Tennessee Eastman (TE) process and actual cement clinker production process were tested and compared with existing RPCA, RCA and RSFA methods. The effectiveness and superiority of the proposed method in the problem of nonstationary transition mode process monitoring are verified.

Corrosion resistance of powder metallurgy fabricated Cu–10Sn/SiC/mica hybrid composite

Abstract For the first time, bronze/SiC/mica hybrid composite has been manufactured using powder metallurgy method. Mixture – process variable design has been applied to design of experiments and optimization of the composite composition, as well as the production process variables (compaction pressure and sintering temperature) to attain superior corrosion resistance. This involved mixing different compositions of bronze, SiC, and mica powders, which were subsequently subjected to varied pressures and temperatures during the pressing and sintering stages, all in accordance with the experimental design plan. The microstructure, chemical composition, and elemental distribution of the samples were examined using scanning electron microscope equipped by energy dispersive X-ray analyzer, and an optical microscope. In order to study the corrosion resistance, potentiodynamic polarization test and electrochemical impedance spectroscopy were performed in 3.5 wt.% NaCl solution. The results revealed that co-incorporation of SiC and mica particles in Cu–10Sn bronze matrix increases the corrosion resistance, with a synergistic effect between these particles. The result of optimization process showed that the highest corrosion resistance could be achieved for the composite with the composition of Cu–10Sn/9.85SiC/0.67mica. This outcome was subsequently validated through experimental procedures.

Development and optimization of Clitoria teratea synthesized silver nanoparticles and its application to nanogel systems for wound healing

Objective The present research deals with sequential optimization strategy based on Central Composite Design to optimize the process variables for efficient production of Clitoria teratea (CLT) synthesized silver nanoparticles (AgNPs) using biological synthesis. Methods Two substantial factors influencing the dependent variables viz UV-visible absorbance, particle size, zeta potential and polydispersity index (PDI) were identified as NaOH concentration, RH concentration, temperature as independent variables. In-vitro and ex-vivo studies of prepared CLT-AgNPs gel and marketed gel were carried out using dialysis membrane and egg membrane, respectively. In addition, antimicrobial study was also performed on the bacterial strains. Results The particles size (114 nm), PDI (0.45), and zeta potential (−29.5 mV) of optimized formulation were found, respectively. In-vitro profile of AgNPs from prepared CLT-AgNPs gel was noted (95.6%) in 8 h. It was found that the prepared CLT-AgNPs gel stimulates fibroblast and agranulocytosis development resulting better and timely wound healing. Conclusions The prepared CLT-AgNPs gel can be as a potential substitute in the management and treatment of acute and chronic wounds.

Statistical optimization of process variables for anti-hypercholesterolemic metabolites production from Monascus purpureus MTCC 369 fermented finger millet

Monascus purpureus fermented rice has been used for centuries to treat hypercholesterolemia, indigestion, and diarrhea in East Asian countries. Monacolin K, also known as lovastatin, a secondary metabolite synthesized by Monascus sp. in fermentation, is mainly responsible for reducing the level of blood lipids. Besides statins, sterols produced in this process have also been reported to have hypolipidemic effects. In the current study, statin and total sterol production by M. purpureus microbial type culture collection and Gene Bank MTCC 369 on finger millet (FM) substrate were investigated. The central composite design of the response surface methodology (RSM) was used for the optimization of different fermentation process independent variables such as fermentation time period, temperature, inoculum volume, and pH for maximum production of statins and total sterols by M. purpureus MTCC 369. The maximum statin (9.78 mg/g) and sterol (0.46 mg/g) production was achieved at a temperature of 29.44°C, pH 5.0, inoculum size 5.37 ml, and fermentation time of 13.8 days. Fermentation process factors such as fermentation time period and temperature were significant positive factors, while pH and inoculum volume were found to be insignificant factors. The interactive effects of the positively influencing variables have been demonstrated. Compared with the traditional production of these metabolites, using FM as a substrate is promising for the high production of anti-hypercholesterolemic metabolites. The current study was aimed at optimization of the solid-state fermentation process of statin and total sterol using RSM for nutritional FM so as to improve M. purpureus-fermented FM (like red yeast rice) with health-promoting properties. This is the first report on the optimization of statin and sterols from M. purpureus MTCC 369 fermented FM with a significant yield that is on par with that reported so far.

Exergetic analysis and optimization of process variables in xylitol production: Maximizing efficiency and sustainability in biotechnological processes

This study presents an exergetic analysis of xylitol fermentative production from hemicellulose hydrolysate, aiming to optimize operational conditions in a fluidized bed bioreactor. The aerobic fermentation conditions evaluated in this study (gas flow rate - x1, hydrolysate concentration factor - x2, and recirculation flow rate - x3) were optimized using various exergetic parameters and xylitol yield as objective functions. Four objective functions were defined for the mono-objective optimization process: rational exergetic efficiency, normalized destroyed exergy, thermodynamic sustainability index, and xylitol yield factor. The results reveal that the optimization problem involves conflicting objectives when considering both yield-based and exergy-based approaches. Thus, the bioreactor's performance was formulated as a multi-objective problem, where the yield factor and thermodynamic sustainability index were simultaneously maximized. For the multi-objective optimization, the ideal operational variable ranges were found to be: 594 ≤x1≤ 619 mL/min, x2= 7 e 37 ≤x3≤ 57 L/h.

Advanced Value Stream Mapping: Development of a Conceptual Model Considering Variability in Production Processes

<div>Recently, lean manufacturing (LM) practices are being combined with tools and techniques that belong to other areas of knowledge such as risk management (RM). Value stream mapping (VSM) is a well-known tool in showing the value, the value stream, and the flow, which represents the three lean principles. VSM and RM, when used in tandem with one another, are more advantageous in covering VSM issues such as the variability of production processes. In this article, a conceptual model that integrates the two is shown and explained. The model helps to generate scenarios of current state map (CSM) and future state map (FSM) in a dynamic way by identifying current and potential risks. These risks might happen in the future, bringing with it negative ramifications including not reaching the main objectives within the defined time. The model has been tested in a coffee production company belonging to health and food sector. The proposed model specified the ranges of variability through the drawing of CSM and FSM. This is quite a milestone because one of the challenges of VSM is that it is a static tool, and, as such, process variability cannot be captured appropriately. This new model is expected to overcome this drawback.</div>

Statistical optimization of process variables for agarase production using Microbacterium sp. SS5 strain from non-marine sources

Agar oligosaccharides are thought to be valuable biomolecules with high bioactivity potential, along with a wide range of applications and advantages. The current study aimed to optimize the culture parameters required to produce agarase enzyme and agar oligosaccharides from industrial waste agar. Microbacterium spp. strain SS5 was isolated from a non-marine source and could synthesize oligo derivatives for use in a variety of industries ranging from food to pharmaceuticals. In addition, the strain and culture conditions were optimized to maximize extracellular agarase production. The bacterium grew best at pH 5.0 − 9.0, with an optimal pH of 7.5 − 8.0; temperatures ranging from 25 to 45 °C, with an optimal of 35 °C; and carbon and nitrogen concentrations of 0.5% each. Plackett-Burman experimental design and response surface methods were used to optimize various process parameters for agarase production by Microbacterium spp. strain SS5. Using the Plackett-Burman experimental design, eleven process factors were screened, and agar, beef extract, CaCl2, and beginning pH were found as the most significant independent variables affecting agarase production with confidence levels above 90%. To determine the optimal concentrations of the identified process factors on agarase production, the Box- Behnken design was used. Agarase production by Microbacterium spp. strain SS5 after optimization was 0.272 U/mL, which was determined to be greater than the result obtained from the basal medium (0.132 U/mL) before screening using Plackett-Burman and BBD with a fold increase of 2.06.

Soft sensing of calcination zone temperature of lime rotary kiln based on principal component analysis and stochastic configuration networks

The combustion state of the lime rotary kiln is mainly reflected in the temperature of the calcination zone. However, due to the high temperature in the calcination zone during the production process, it is not possible to directly measure using instruments. This paper proposes a soft sensing strategy to address the issue of difficulty in measuring the temperature of the calcination zone of a lime rotary kiln. Firstly, due to significant disturbances and coupling of production process variables, principal component analysis is used to reduce the input dimension and computational complexity of the data, thereby improving the efficiency of the soft sensing model. Then, stochastic configuration networks with good predictive performance is used to model and predict the principal components. Meanwhile, an improved Hammersley-based Salp swarm algorithm is proposed to optimize the key parameter regularization factor of the stochastic configuration networks. Finally, the validity of the soft sensing model is verified by the actual collected production data of the lime rotary kiln. Multiple evaluation indicators and comparison results indicate that the proposed soft sensing model for the calcination zone temperature of lime rotary kilns has higher prediction accuracy and modeling quality compared to other models.

The Contribution of Lean Management—Industry 4.0 Technologies to Improving Energy Efficiency

The current socio-economic and environmental context obliges companies to increase their energy efficiency to be competitive, and the development of Industry 4.0 technologies should contribute to improve it. This article analyses the influence of Industry 4.0 technologies on energy efficiency and the mediation of quality management of production process variables. After a descriptive analysis, a correlation and regression analysis is presented using information from 72 projects for the integration of Industry 4.0 technologies in industrial companies. At a global level, it is confirmed that the four technology groups (Artificial Vision and Artificial Intelligence, Additive Manufacturing and Robotics, Big Data and Advanced Analytics, and Internet of Things) contribute to improving energy efficiency by an average of 15–25% in the processes where they are integrated. In addition, the regression model determines that improved decision-making capabilities strongly mediate the achievement of higher energy efficiency.

Modeling of Artificial Neural Networks for Hydrogen Production via Water Electrolysis

Artificial neural networks have emerged as a promising tool for estimating hydrogen production process variables for reaction condition optimization. Here we aim to predict complex nonlinear systems that use of artificial neural networks for modeling hydrogen production via water electrolysis and to evaluate the common challenges that arise. To estimate the effect of different electrolyzer systems input parameters such as electrolyte material, electrolyte type, supplied power (voltage and current), temperature, and time on hydrogen production, a predictive model was developed. The percentage contributions of the input parameters to hydrogen production and the best network architecture to minimize computation time and maximize network accuracy were shown. The results show that the hydrogen production parameters from electrolysis and the predicted safety explosive limit are 7% of the average root mean square error. Furthermore, coefficient of determination value was found 0.93. This predicted value is very close to the observed values. The neural network algorithm developed in this study could be used to make critical decisions in the electrolysis process for parameters affecting hydrogen production.

Soft-sensing modeling of mother liquor concentration in the evaporation process based on reduced robust least-squares support-vector machine.

The evaporation process is vital in alumina production, with mother liquor concentration serving as a critical control parameter. To address the challenge of online detection, we propose the introduction of a soft measurement strategy. First, due to the significant fluctuations in the production process variables and inter-variable coupling, comprehensive grey correlation analysis and kernel principal component analysis are employed to reduce the input dimension and computational complexity of the data, enhancing the efficiency of the soft sensing model. The reduced robust least-squares support-vector machine (LSSVM), with its commendable predictive performance, is used for modeling and predicting the principal components. Concurrently, an improved Pattern Search-Differential Evolution (PS-DE) algorithm is proposed for optimizing the pivotal parameters of the LSSVM network. Lastly, on-site industrial data validation indicates that the new model offers superior tracking capabilities and heightened accuracy. It is deemed aptly suitable for the online detection of mother liquor concentration.

Environmental impact assessment of alkali-activated materials: Examining impacts of variability in constituent production processes and transportation

This study compares the environmental impacts of blast furnace slag-, fly ash-, and metakaolin-based alkali-activated concretes with Portland cement (PC) concretes using life cycle assessment methodology. The variability in production processes of mix constituents across Europe and uncertainty associated with transportation distances are evaluated for both types of concrete. The recent data on soluble sodium silicates production was obtained from European manufacturers. Results show that alkali-activated materials have up to 57 % lower CO2 eq. emissions than PC concretes, while activators contribute between 13 % and 33 % to the total GWP of AA concrete mixes, depending on mix design. This paper concludes that taking into account variability in production technologies of precursors and activators, as well as of PC, alkali-activated materials still have lower CO2 eq. emissions than PC concretes.

Optimization of bacteriocin production by Lactobacillus plantarum using Response Surface Methodology.

Bacteriocin production is influenced by various factors such as carbon and nitrogen sources as well as fermentation conditions including pH, temperature, and agitation-these factors aid in optimizing bacteriocin production and improving its inhibitory activity against pathogens for great economic significance. The study investigates the effect of growth conditions on bacteriocin production by Lactobacillus plantarum. The response surface methodology was applied to optimize and determine the interaction among the process variables in bacteriocin production by Lactobacillus plantarum and determine theiroptimumlevels. Chloroform-Methanol (2:1 v/v) was used for crude bacteriocin extraction through the liquid-liquid extraction method, and its antimicrobial activity was assessed. The sample has shown inhibitory activity against all the organism,s i.e., E. coli, S. aureus, K. pneumoniae and P. aeruginosa good diffusion. Compared to control and ciprofloxacin, the sample at all concentrations (Lab 9, 1%, 2%, 3%) has shown better inhibitory activity in pH-7 and-8 at 37 °C against S.aureus in good diffusion. The selected factors were carbon and nitrogen source, temperature, and pH. The bacteriocin was produced at maximum activity in MRS broth supplemented with 1% dextrose and 1% ammonium nitrate. RSM analysis found that the optimal temperature for bacteriocin production was 36°C at a pH of 6.5 using 1% inoculum. At the same time, the increase in the percentage of inoculum (2% and 3%) did not affect bacteriocin production. The quadratic model found that temperature and pH profoundly affected bacteriocin production.

녹차씨 오일을 이용한 바이오디젤 특성에 관한 연구

The purpose of this work was to examine the reaction time, reaction temperature, catalyst, and mixing ratio of methanol and Ungpo tea seed oil extracted from supercritical CO2 extraction method for optimization of the biodiesel production process variables. The optimum conditions for effective biodiesel production were obtained at 50 min of reaction time, 60°C of reaction temperature, 1.0% of KOH as catalyst, and mixed molar ratio 1:5 of tea seed oil and methanol, respectively. For evaluation of biodiesel produced from the optimum conditions, physicochemical properties were measured. Biodiesel production yield was a range of 97.14 and 97.79%. In the case of density, it was a range of 0.83 to 0.84, which was similar to standard value. When Ungpo tea seed oil's biodiesel was used, the iodine value was 111.25 g I2/100 g, which was about 5.4% lower than Jeju tea seed oil's biodiesel. Oxidative stability of Ungpo tea seed oil's biodiesel was found to be 7.58 hr, which was about 5.0% lower than Jeju tea seed oil. However, compared to the EU's biodiesel quality standard, it was increased by about 20.3%. CFPP of Ungpo tea seed oil's biodiesel was found to be -8.38 ∼ -7.84℃, which was almost similar as that of Jeju tea seed oil's biodiesel. These results indicated that Ungpo tea seed oil exracted from supercritical CO2 extraction might be a suitable raw material for the biodiesel production.

Electro‐conductive nanofibrous structure based on PGS/PCL coated with PPy by in situ chemical polymerization applicable as cardiac patch: Fabrication and optimization

AbstractAs a special structure and due to the unique properties of submicron‐scale fibers and electrical conductivity, conductive nanofibers recently attracted further attention in various fields, especially tissue engineering. This study aimed to coat poly (glycerol sebacate) (PGS) nanofiber/poly(caprolactone) (PCL) as a widely used combination in the fields of medicine with poly(pyrrole) (PPy) as a conductive polymer, subsequently studying and optimizing production process variables using response surface methodology (RSM). The samples were examined for morphological and structural, and their electrical conductivity and electroactivity were measured using a four‐point probe system and a cyclic voltammetry, respectively. The electrospinning conditions for constructing the substrate nanofibers with the smallest diameter were optimized. The monomer concentration of 0.05M and the polymerization time of 3.3 h exploiting the RSM were also considered as the optimal conditions for obtaining coated‐nanofibers with the smallest diameter and desired electrical conductivity (0.001 S/cm). The structural findings confirmed the successful synthesis of PGS and PPy. The conductive‐nanofiber produced under optimal conditions revealed good mechanical properties (tensile strength = 3.12 ± 0.19 MPa and elongation at break = 80.26 ± 13.71%). The highest biocompatibility was observed for the sample synthesized with the pyrrole concentration of 0.05M, which can be a suitable candidate for application in cardiac tissue engineering.

A collaborative design platform for new alloy material development

To overcome the shortcomings of the conventional trial and error mode for new material development, a full-process collaborative design platform for steel rolling is developed based on an industrial internet of things (IIoT) system in this study. Equipment, process and product entities are modeled in both the physical domain and the cyber domain. A systematic data-driven Mamdani-type fuzzy modeling methodology is proposed to map the relationship between material chemical compositions, organizational structures, process parameters and mechanical performances. The proposed methodology employs a random forest (RF) algorithm to select important parameters from mechanism models, simulation models and production process variables, utilizes a K-means algorithm to merge diverse steel grades into sub-clusters, and implements a multi-objective particle swarm optimization (MOPSO) algorithm to further improve the fuzzy model in terms of both the structure and the membership function parameters. A dataset of 3500 steel coils collected by the prototype platform built in a large hot rolling mill is used to evaluate the performance of the proposed approach. Experiment results show that the proposed methodology performs well in predicting the yield strength, tensile strength and elongation, with the coverage probability over 90% under 10% deviation and about 70% under 5% deviation on average.

Biotechnology approach using watermelon rind for optimization of α-amylase enzyme production from Trichoderma virens using response surface methodology under solid-state fermentation.

Production of amylases by fungi under solid-state fermentation is considered the best methodology for commercial scaling that addresses the ever-escalating needs of the worldwide enzyme market. Here response surface methodology (RSM) was used for the optimization of process variables for α-amylase enzyme production from Trichoderma virens using watermelon rinds (WMR) under solid-state fermentation (SSF). The statistical model included four variables, each detected at two levels, followed by model development with partial purification and characterization of α-amylase. The partially purified α-amylase was characterized with regard to optimum pH, temperature, kinetic constant, and substrate specificity. The results indicated that both pH and moisture content had a significant effect (P < 0.05) on α-amylase production (880 U/g) under optimized process conditions at a 3-day incubation time, moisture content of 50%, 30°C, and pH 6.98. Statistical optimization using RSM showed R2 values of 0.9934, demonstrating the validity of the model. Five α-amylases were separated by using DEAE-Sepharose and characterized with a wide range of optimized pH values (pH 4.5-9.0), temperature optima (40-60°C), low Km values (2.27-3.3mg/mL), and high substrate specificity toward large substrates. In conclusion, this study presents an efficient and green approach for utilization of agro-waste for production of the valuable α-amylase enzyme using RSM under SSF. RSM was particularly beneficial for the optimization and analysis of the effective process parameters.

An approach for the identification of production process variables in cross-process chain production processes like battery cell production

Europe is currently not competitive in battery cell production. In order to increase competitiveness, battery cell production must be made more efficient. A major factor in improving efficiency is the reduction of waste. This requires understanding the many dependencies between the production process variables within battery cell manufacturing. For clarifying these dependencies, knowledge of the variables is essential. The challenge here is to completely determine them. For this purpose, different tools and methods are applied. But, in the case of cross-process chain production processes like battery cell production, they quickly reach their limits because these tools are not suitable for the structure and properties of the respective processes. The aim of the approach presented here is to support a complete identification of all process variables of battery cell production in the best possible way.