Effect Of Process Variables Research Articles

Biodiesel production using microreactor with integrated microheater through multi-objective optimization approach

Microreactor is one of the process intensification techniques which uses reaction dimensions below millimeter size and have greater yield within lesser reaction time and a high heat transfer rate due to their high surface to volume ratio. To develop energy efficient reactors, microheater is chosen as the heating source for microreactor which consume less power than the conventional heating elements. Using response surface methodology (RSM) and artificial neural network (ANN) the effects of the biodiesel process variables was analyzed: catalyst concentration (0.5–2.5 wt.%), reaction temperature (35–55 °C), flow rate (5–25 mL/min), oil to methanol molar ratio (1:6–1:18), and power supplied to the microheater (15–35 W). To observe more than set limit, Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and VIKOR were used to evaluate the optimum conditions. Under the optimum conditions of 20 mL/min flow rate, 45 °C reaction temperature, 1.5 wt.% catalyst concentration, 1:9 oil to methanol molar ratio, and 20 W power supplied, the maximum biodiesel yield for microreactor with microheater is 91.1 %. Using ultrasonic mixing additionally for microreactor with microheater had maximum yield of 94.4 % under the optimum conditions.

Fracture morphology and strength characteristics of poly-lactic acid and poly-ethylene terephthalate glycol composites combined with taguchi method and response surface methodology

The emergence of additive manufacturing has enabled scientists to efficiently construct complex geometries, facilitating the development of novel, high-impact energy-absorbing structures suitable for a wide range of industrial applications. The present study conducted flexural and impact test to quantitatively assess the energy absorption capabilities of polymer composites fabricated through fused filament fabrication. Specifically, the polymer composites investigated were multi-walled carbon nanotubes reinforced poly-lactic acid, carbon fibre reinforced poly-ethylene terephthalate glycol, and carbon fibre reinforced poly-lactic acid. The investigation also examined the influence of different infill patterns and nozzle hole diameters on the polymer composites. The investigation depicts that by altering the process parameters, the flexural strength is improved from 21.079 MPa to 70.653 MPa by 235.18%. The experimental study of impact specimens utilising the Izod impact test demonstrates that the rectilinear infill pattern and a nozzle hole diameter of 0.6 mm result in the highest energy absorption of 37.76 kJ/m2 for carbon fibre reinforced poly-lactic acid. The study revealed that the energy absorption of the specimens was significantly influenced by both the independent and interaction effects of process variables. The application of the fused filament fabrication demonstrates an improved energy absorption, making it suitable for manufacturing of vehicle and aircraft components.

Effect of process variables on leaching behavior and kinetics of silver element from waste photovoltaic modules

In order to achieve efficient leaching of silver, in waste photovoltaic panels, the leaching process using nitric acid was investigated. The effects of nitric acid concentration, leaching time, solid–liquid ratio, and leaching temperature on the silver leaching rate were examined. The response surface method was used to establish an experimental model for analyzing and optimizing the leaching experiment. The results showed that the nitric acid concentration had the most significant effect on the leaching rate. The leaching rate of 98.02% was achieved under the following optimal leaching conditions: a nitric acid concentration of 18%, a leaching time of 32 min, a solid–liquid ratio of 1:30, and a leaching temperature of 60 °C. According to the shrinkage nuclear model, the leaching kinetics of silver using nitric acid followed a chemical reaction-controlled process with an apparent activation energy of 42.26 kJ/mol. Scanning electron microscopy–energy-dispersive x-ray spectrometry results of minerals before and after leaching confirmed the efficient recovery of silver in waste photovoltaic cells.

Effects of Process Variables on the Physicochemical, Textural, and Structural Properties of an Isolated Pea Protein-Based High-Moisture Meat Analog.

This study investigated the optimal extrusion conditions required to produce an isolated pea protein (IPP)-based meat analog. High-moisture extrusion cooking (HMEC) was performed. The effects of the moisture content (55 and 60%), barrel temperature (165 and 175 °C), and screw speed (150 and 200 rpm) on the physicochemical, textural, and structural properties of the high-moisture meat analog (HMMA) were determined. The results showed that the moisture content had a significant effect (p < 0.05) on the physicochemical and textural properties of the HMMA. A lower moisture content had significant impact (p < 0.05) on enhancing the texturization of the HMMA and the formation of fibrous structures, thereby increasing the texture profile analysis (TPA) and cutting strength of the HMMA. Protein denaturation during HMEC resulted in a lower protein solubility of the meat analog than the raw material. The content of β-sheets and β-turns in the meat analogs were higher than that in the raw material, while the content of random coils and α-helices is inversely proportional. The process variables had no significant (p > 0.05) effect on the secondary structures. In conclusion, the moisture content is the most important factor affecting the properties of HMMAs. The extrusion process variables for HMMAs are a moisture content of 55%, a barrel temperature of 175 °C, and a screw speed of 200 rpm.

Effect of Process Variables on Texture and Hydride Orientation of Cold Pilgered Zr-Sn-Nb-Fe Cladding Tubes

Effect of Process Variables on Texture and Hydride Orientation of Cold Pilgered Zr-Sn-Nb-Fe Cladding Tubes

Lactic Acid-Proline Solvent Pretreatment of CS: Effects of Process Variables on Glucan, Xylan, and Lignin Composition

The aim of this study is to investigate the effect of temperature, duration, and water dilution on the composition of glucan, xylan, and lignin in cowpea shells (CS) pretreated with a neat and diluted Lactic acid-proline solvent. The composition of glucan, xylan, and lignin was analyzed using the NREL method. The highest xylan removal (12%) was achieved after a 6-hour pretreatment at 150°C, while 11.35% lignin removal was observed after 5 hours at the same temperature. The most significant increase in glucan content, reaching 78.3 %, was observed after 4 hours at 150°C using a 2.5% (w/w) water-diluted solvent. Comparing the effect of the neat solvents and diluted solvents it can be concluded that while the neat solvent promoted the dissolution of xylan and lignin, the addition of water preserved glucan from the harshness of the pretreatment. Therefore, the decision to dilute a natural deep eutectic solvent before its application in biomass pretreatment depends on the desired product from biomass fractionation (lignin or a carbohydrate-rich material) and pretreatment temperature. These findings provide a foundation for further investigations into optimizing the entire process.

Using machine learning algorithms to predict the effects of change processes in psychotherapy: Toward process-level treatment personalization.

This study aimed to develop and test algorithms to determine the individual relevance of two psychotherapeutic change processes (i.e., mastery and clarification) for outcome prediction. We measured process and outcome variables in a naturalistic outpatient sample treated with an integrative treatment for a variety of diagnoses (n = 608) during the first 10 sessions. We estimated individual within-patient effects of each therapist-evaluated process of change on patient-evaluated subsequent outcomes on a session-by-session basis. Using patients' baseline characteristics, we trained machine learning algorithms on a randomly selected subsample (n = 407) to predict the effects of patients' process variables on outcome. We subsequently tested the predictive capacity of the best algorithm for each process on a holdout subsample (n = 201). We found significant within-patient effects of therapist perceived mastery and clarification on subsequent outcome. In the holdout subsample, the best-performing algorithms resulted in significant but small-to-medium correlations between the predicted and observed relevance of therapist perceived mastery (r = .18) and clarification (r = .16). Using the algorithms to create criteria for individual recommendations, in the holdout sample, we identified patients for whom mastery (14%) or clarification (18%) were indicated. In the mastery-indicated group, a greater focus on mastery was moderately associated with better outcome (r = .33, d = .70), while in the clarification-indicated group, the focus was not related to outcome (r = -.05, d = .10). Results support the feasibility of performing individual predictions regarding mastery process relevance that can be useful for therapist feedback and treatment recommendations. However, results will need to be replicated with prospective experimental designs. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Enhanced cellulose extraction from agave plant (Agave americana Species) for synthesis of magnetic/cellulose nanocomposite for defluoridation of water

Research on fluoride removal from water is currently focusing on the development of innovative materials for defluoridation water. The current study extracted and used enhanced cellulose from Agave americana species to synthesize a magnetic/cellulose nanocomposite for water defluoridation. Strong and light binary acids (H2SO4 and CH3COOH) were utilized to pretreat raw material to enhance cellulose extraction. Central composite design (CCD) was used to design experiments to find optimum condition for cellulose extraction. Four key factors (H2SO4 to CH3COOH ratio, acid concentration, temperature, and contact time) with three levels were designed by CCD. The lignin, hemicellulose and cellulose content of raw agave cellulosic fiber in this study were 5.5 ± 0.27, 20.5 ± 0.23 and 60.4 ± 0.31 % respectively. While, at the optimum conditions of 2.3 % acid concentration, 0.44 H2SO4 to CH3COOH ratio, 83.2 min of retention time, and 105.5 oC, cellulose content reached 940.25 %. Then, enhanced cellulose was covered with magnetic components, resulting in a magnetic/cellulose nanocomposite. The adsorbent materials were characterized by Fourier Transform Infrared (FTIR) spectroscopy, X-Ray Diffractometer (XRD), Scanning electron microscopy (SEM), and Dynamic light scattering (DLS). In addition, textural features of the adsorbent were investigated. All characterization results suggest that the synthesized adsorbent has the required properties. Adsorption tests were carried out using various interaction components. The combined effects of critical process variables on defluoridation were examined. The adsorption isotherm was calculated. Overall, the research demonstrated that the produced magnetic/cellulose nanocomposite can be employed as an efficient and ecologically acceptable adsorbent for fluoride removal from water.

Investigation of functional and sensory properties of plantain flour in citric acid

Purpose: This study investigated the effects of process variables on the proximate, functional, and sensory properties of plantain flour.&#x0D; Research Methodology: The plantain fruit was sorted and hand-peeled using kitchen knives. It was then cut into various slices of 2 – 6 mm and steeped in citric acid solutions with concentrations ranging between 1 – 5 %. The steeping time varied between 30 – 120 min. The pretreated plantain slices were processed into flours. Process parameters were analyzed using the response surface methodology of Design Expert software.&#x0D; Results: The proximate parameters of the flour samples showed no significant difference (p &gt; 0.05), but there were significant effects (p&lt; 0.05) on the functional and sensory properties of the flour samples. The functional properties were found to differ significantly.&#x0D; Limitation: This study did not consider the application of other optimization methods such as genetic algorithm and particle swarm optimization in estimating the optimum points. Future studies could focus on these areas.&#x0D; Contribution: The validation of the optimization processes showed success in the application of citric acid in the production of novel plantain flour.

Avocado Hass peel from industrial by-product: effect of extraction process variables on yield, phenolic compounds and antioxidant capacity

At present in Peru, a substantial quantity of avocado by-products (peel and seed) is produced due to avocado processing. It is essential to revalue these products. The meticulous selection of parameters in solid–liquid extraction through maceration, which is the industrial preference, is crucial to obtain a greater recovery of phenolic compounds from avocado Hass peels. Therefore, the aim of this study was to assess the impact of process variables (ethanol concentration, temperature and solvent:feed (S/F) ratio) on the global yield of extraction (GY), total phenolic content (TPC), total flavonoid content (TFC), and antioxidant capacity (DPPH) of avocado Hass peel extract at two different stages of maturity. Furthermore, the quantification of phenolic compounds was using High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD) and antioxidant capacity using ABTS and FRAP assays were determined in higher yielding extracts. The dry matter content (dm) was determined in the pulp of unripe avocados (UA, 22.57% dm) and ripe avocados (RA, 27.30% dm). The results showed that, for all treatments, ethanol concentration, temperature, S/F ratio had a significant influence (p &amp;lt; 0.05) on GY, TPC, TFC and antioxidant capacity, and the UA peel extracts obtained with 40% ethanol, 49.3°C, S/F ratio (14.3 mL/g) and 60 min showed the highest values of TPC (44.24 mg GAE/g peel dw), TFC (786.08 mg QE/g peel dw) and antioxidant capacity against DPPH (564.82 μmTE/g peel dw), ABTS (804.40 μmTE/g peel dw) and FRAP (1006.21 μmTE/g peel dw). Meanwhile, for the HPLC-DAD analysis, vanillic acid and 4-hydroxyphenylacetic acid are the main phenolic compounds present in avocado peel extracts. The results of this research indicate that avocado peels are a source of natural phenolic components, with potential application in the food industry as a viable alternative to synthetic antioxidants, thus reducing their use.

Energy-based performance prediction for metals in powder bed fusion

The mechanical performance of metallic parts fabricated by powder bed fusion (PBF) additive manufacturing is directly linked to process variables. However, it remains challenging to rapidly forecast the resulting mechanical performance based on specified process conditions. To tackle this issue, this paper proposes a process-performance prediction model capable of efficiently estimating the yield strength (YS) and ultimate tensile strength (UTS) via used process parameters. The formation quality of metals related to energy absorptivity/consumption is first determined based on the energy balance principle during PBF. Subsequently, the effective properties of as-built metals with process-induced defects are obtained using a homogenization method. Considering the relation between the thermal energy and elastoplastic strain energy, the YS and the UTS of printed parts are effectively predicted based on the force-heat equivalence energy density principle. The accuracy of the proposed model is validated by the comparison with the literature. Furthermore, the effect of the main process variables on the YS and the UTS of printed metallic parts is demonstrated and analyzed. The results show an increase of YS and UTS followed by a gradual decrease with the key process variables increasing, including the volumetric energy density, length of scan vector, layer thickness, and environment temperature. These results can serve as a guideline for improving the mechanical performance of PBF-printed metals.

Applications of genetic algorithm in prediction of the best achievable combination of hardness and tensile strength for graphene reinforced magnesium alloy (AZ61) matrix composite

Due to lack of knowledge about role of process variables in the hot rolling of magnesium alloy composites, the most desirable wrought products of magnesium alloy-based composites could not be successfully produced till date. Moreover, use of wrought graphene reinforced magnesium alloy-based composite has emerged as a lucrative option for developing novel light alloy products in structural applications. The implicative need to obtain the best possible properties of the composite dictates to fix the optimized process variables in rolling operation. The present study envisages the development of data driven model that enables to derive desirable properties of the said composite; so, in order to secure the optimized subset of requirements (process parameters), a metaheuristic optimization tool is employed. We invoke the use of Genetic Algorithm (GA) optimizing tool in association with linear regression, so as to achieve the best combination of hardness and tensile strength of AZ-61-graphene nanoplate (GNP) composite.Modified magnesium AZ-61 alloy composites of varying GNP content (0, 1 and 2 wt.%) are produced by stir casting. The cast composites are homogenized at 450 °C for 12 h, followed by iced water quenching. The quenched materials are subjected to hot rolling and the process variables of hot rolling operation consist of rolling temperature, number of rolling passes as well as the initial stock thickness. The rolled materials are tested for hardness and tensile strength as the response variables. Instead of using artificial neural network to fix the objective function of GA, a mathematical model is developed by regression analysis; the derived equation is used as the objective function for multi-objective optimization task. The present paper explores the usefulness of implementing evolutionary algorithm (as in genetic algorithm) to correctly forecast the effect of rolling process variables onto the hardness and tensile strength of the stir cast AZ alloy-GNP nanocomposite.The experimental results show that the maximum achievable hardness value happens to be 106.6 VHN and is very close to that predicted by GA, ∼107.41VHN. Similarly, the maximum experimental tensile strength is found to be 266.82 MPa whereas, the corresponding GA predicted value is seen to be 253 MPa. A close fit can thus be noticed between the GA predicted and the experimentally obtained values. Barring a couple of exceptions, the difference between the experimental and GA predicted data lies within 1 % to 5 % (based on [Experimental value ∼ predicted value]/ [experimental value]). This entices to believe that the adopted muti-objective optimization strategy is quite useful in determining the optimum combination of rolling parameters that aids in achieving the most desirable property combination.

A predictive model for the techno-economic assessment of CO[formula omitted] chemisorption processes applicable to a large number of amine solvents

With the growing need to reduce carbon dioxide (CO2) emissions, there have been substantial efforts to identify new solvents that can improve the overall performance of chemical-absorption CO2 capture processes. Given the large number of potential solvents, computer-aided molecular and process design (CAMPD) approaches can play a critical role in accelerating the search for optimal solvents by enabling the systematic exploration of solvent candidates and process conditions. One of the challenges in developing such a framework is the requirement for a process model that can be used to capture the interactions between process performance and solvent structure without significantly increasing its numerical complexity. In the current work, a model for the absorption–desorption of CO2 is developed using the predictive SAFT-γ-Mie group-contribution approach, allowing the performance of numerous solvents to be assessed without the need for extensive experimental data. In order to avoid convergence difficulties when solving this highly nonlinear model, a tailored initialization strategy is established, using an adapted inside-out algorithm to prime a nonlinear equation solver for each column, providing a good initial guess for the whole flowsheet. Tests on three solvents confirm the robustness of the approach. Building on this enhanced numerical stability, the model is validated by comparison against pilot-plant data, showing good accuracy. A detailed parametric study of the effect of the key process variables is undertaken; the important role of the CO2 capture rate, of the lean solvent temperature and loading, and of the desorber pressure is highlighted. The results of the parametric study are used to formulate an optimization problem which is successfully solved for four solvents. A large reduction in total annualized cost and energy requirements is achieved by tuning the operating conditions to each solvent considered. The predictive capability, robustness, and reliability of the proposed model and associated initialization strategy open the way for the evaluation of a large number of novel solvents.

Modeling and biosorption potential of Aspergillus heteromorphus for treatment of lead contaminated wastewater

Dead biomass of the fungus species Aspergillus heteromorphus was used to remove Pb (II) ions from electroplating industrial effluents. The optimum process variables for biosorption of Pb (II) ions from aqueous and lead contaminated wastewater were determined using Response Surface Methodology (RSM). These variables included temperature (20–40 °C), metal ions concentration (20–100 mg/L), pH (2.0–6.0), and biomass dose (0.5–2.5 g/L). A total of 29 Box–Behnken design matrix RSM experiments were undertaken for this purpose. The studies were designed using a quadratic model to investigate the interacting effects of process variables. The significance of independent variables was 0.0001, indicating that they are important in the biosorption process. Model terms that have a value of "Prob > F" < 0.0500 are significant for biosorption. The best temperature was observedas 32 °C, along with a biomass dose of 2.4 g/L at pH 5.0, and with the initial concentration of Pb (II) ions (65 mg/L) was found to be optimum for biosorption. A. heteromorphus was able to remove Pb (II) ions up to 91% from aqueous solution under above mentioned ideal conditions. However, from electroplating industrial effluent the Pb (II) ions removal efficiency of A. heteromorphus was observed as 32%.

APPLICATIONS OF MATHEMATICAL MODELLING IN PHARMACEUTICAL FORMULATION AND PROCESS DEVELOPMENT

Pharmaceutical development and authorization stages have high requirements that increase labour and costs. Risks for product quality and process robustness also increase in parallel with complex practices existing in the pharmaceutical industry and emerging as a result of developments. Although it is challenging to eliminate parameters leading to increased risks, there is need to appropriately manage the risks likewise arrange decision making processes. Designing and optimizing formulation and production processes to deliver the predetermined product quality is known as Quality by Design (QbD) in pharmaceutical development. In terms of data and knowledge, QbD can be carried out using a variety of technologies in this process. Mathematical modelling is one of these tools allows for the quick formation of subject knowledge, which may subsequently be used in an independent or integrated manner and to produce Design of Experiments (DOE). Response surface method (RSM), Artificial Neural Network (ANN), Genetic Algorithm (GA) are some of the assistive technologies used in mathematical modelling that enables to enlighten the effect of formulation and process variables on product quality attributes. The use of advanced mathematical modelling techniques in formulation and process development has become widespread and it appears to be beneficial in different areas of pharmaceutical development.

Effect of process parameters on friction stir welding of aluminium alloy AA6082–T6 with an interlayer using response surface methodology

In the present study, the effects of variation of process parameters on the mechanical properties of friction stir welded A6082–T6 aluminium alloy joints with Zinc (Zn) interlayer are experimentally investigated. Four process parameters, viz. rotational speed, welding speed, interlayer thickness and tool pin diameter are selected to fabricate the Friction Stir Welding (FSW) joints. After filling the Zn particles into the gap maintained between the plates, FSW is performed using different process parameters. Thereafter, tensile and impact strength tests are performed to evaluate the performance of the joints. It is analyzed that the tensile and impact strength of the joints vary from 121 to 197 MPa and 6 to 17 J, respectively. Subsequently, mathematical models are developed using Response Surface Methodology (RSM) to predict the effect of FSW process parameters on the tensile and impact strength of joints. Analysis of Variance (ANOVA) is used to check the level and degree of direct effect of process variables on the tensile and impact strength of the joints. It is observed from the results that tool rotational speed is the most influential process parameter, which enhances the mechanical properties of FSW joints, whereas an increase in welding speed reduces the mechanical properties of the joints.

Decomposition of the antibiotic sulfamethoxazole by the liquid-phase plasma process enhanced by peroxymonosulfate

In this study, sulfamethoxazole (SMZ) was successfully degraded using the liquid phase plasma (LPP) method, a new advanced oxidation process (AOPs). The effects of LPP process variables and side reactions on SMZ decomposition reactions were evaluated, and optimal LPP process conditions were determined based on these data. To increase the efficiency of the SMZ decomposition reaction, peroxymonosulfate (PMS) was added to the LPP reaction. The optimal addition amount was derived by tracking the change in the amount of oxidation radicals produced according to the addition of PMS. By analyzing the reaction intermediates generated in the SMZ decomposition reaction, the decomposition pathway of SMZ by the LPP method was proposed.

Effect of Process Variables and Ingredients on Controlled Protein Network Creation in High-Moisture Plant-Based Meat Alternatives.

The market has observed a rapid increase in the demand for plant-based foods as an alternative to animal meat products. Technologies such as high-moisture extrusion (HME) have the potential to develop anisotropic structures using alternative protein ingredients. This article discusses the different possible mechanisms responsible for structure formation and the effect of extrusion process parameters and outlines the recent advances in the long cooling dies (LCDs) used for meat alternative development. The role of different protein ingredients and the impact of combining them with other biopolymers were also evaluated. The underlying mechanism behind anisotropic structure formation during HME is a synergistic effect, with substantial dependence on the source of ingredients and their processing background. Formulation including proteins derived from plants, insects, animals, and microalgae with other biopolymers could pave the way to develop structured meat alternatives and fill nutritional interstices. Dynamic or rotating annular gap cooling dies operating at freely controllable shear and static annular gap dies are recent developments and assist to produce layered or fibrous structures. The complex chemical sites created during the HME of plant protein favour flavour and colour retention. This paper summarises the recent information published in the scientific literature and patents, which could further help researchers to fill the present knowledge gaps.

Associations between the Bacterial Composition of Farm Bulk Milk and the Microbiota in the Resulting Swedish Long-Ripened Cheese.

The maturation of a traditional Swedish long-ripened cheese has shown increasing variation in recent years and the ripening time is now generally longer than in the past. While the cheese is reliant on non-starter lactic acid bacteria for the development of its characteristic flavour, we hypothesised that the observed changes could be due to variations in the microbiota composition and number of bacteria in the raw milk used for production of the cheese. To evaluate associations between microbiota in the raw milk and the resulting cheese, three clusters of commercial farms were created to increase variation in the microbiota of dairy silo milk used for cheese production. Cheese production was performed in three periods over one year. Within each period, milk from the three farm clusters was collected separately and transported to the cheese production facility. Following pasteurisation, the milk was processed into the granular-eyed cheese and matured at a dedicated cheese-ripening facility. For each cheese batch, farm bulk and dairy silo milk samples, a starter culture, early process samples and cheese samples from different stages of maturation (7-20 months) were collected and their microbiota characterised using 16S rRNA amplicon sequencing. The microbiota in the farm bulk milk differed significantly between periods and clusters. Differences in microbiota in dairy silo milk were observed between periods, but not between farm clusters, while the cheese microbiota differed between periods and clusters. The top 13 amplicon sequence variants were dominant in early process samples and the resulting cheese, making up at least 93.3% of the relative abundance (RA). Lactococcus was the dominant genus in the early process samples and, together with Leuconostoc, also dominated in the cheese samples. Contradicting expectations, the RA of the aroma-producing genus Lactobacillus was low in cheese during ripening and there was an unexpected dominance of starter lactic acid bacteria even at the later stages of cheese ripening. To identify factors behind the recent variations in ripening time of this cheese, future studies should address the effects of process variables and the dairy environment.

Optimization using Taguchi Method to Investigate the Effects of Process Parameters on the Hardness of Developed Aluminium Roofing Sheets

This study focused on the optimization using taguchi method to ınvestigate the effects of process parameters on the hardness of developed aluminium roofing sheets. Maximizing process variables like manufacturing temperature, manufacturing pressure, cooling time, and percentage of magnesium in aluminium, the Taguchi Method, a statistical method, was used to enhance the manufacturing quality of aluminium roofing sheets. Using an orthogonal array, a signal-to-noise ratio, and an analysis of variance, the effects of process variables on the hardness of the produced aluminium roofing sheets were examined. In this analysis, four factors; manufacturing temperature, manufacturing pressure, cooling time, and the precentage of magnesium in the aluminium roofing sheet were investigated. Thus, a suitable orthogonal array was selected, and experiments were conducted. Following the trial, the process parameters were evaluated, and the signal-to-noise ratio was calculated. The best parameter values were determined with the use of graphs, and confirmation trials were performed. The results showed that an aluminium roofing sheet's maximum hardness of 65.0kgf was obtained at a manufacturing temperature of 1250 °C, a manufacturing pressure of 65 GPa, a cooling period of 95 seconds, and a magnesium content of 0.5%. The most important influences on the hardness of aluminium roofing sheets was found to be the precentage of magnesium in aluminium roofing sheets followed by manufacturing pressure and manufacturing temperature. The cooling time was found to be the least efficient one. The obtained results in this study were used to improve the material property (hardness) of aluminum roofing sheet and investigate the effects of production factors in aluminum production industries.