Characterization and optimization of eco-friendly gypsum materials using response surface methodology

Nevirapine (NVP), used for the treatment of HIV/AIDS, exhibits unpredictable oral bioavailability, has a poor side effect profile and requires frequent dosing. Niosomes are novel drug delivery systems that have the potential to overcome these challenges. A thin layer hydration approach was used to produce niosomes and optimisation was undertaken using design of experiments (DoE) and response surface methodology (RSM) establish and identify parameters that may affect the manufacture of niosomes. The impact of cholesterol and surfactant content, hydration time and temperature on manufacture was investigated. Critical quality attributes (CQA) in respect of particle size (PS), entrapment efficiency (EE), polydispersity index (PDI) and the amount of NVP released at 48 hours was also assessed. The optimised niosome composition was identified and manufactured and the CQA characterised prior to placing the batch on stability for 12 weeks at 4±2 °C and 22±2 °C. The PS, PDI, EE and % NVP released at 48 h was 523.36±23.16 nm, 0.386±0.054, 96.8 % and 25.3 % for niosomes manufactured with Span® 20. Similarly, the parameters were 502.87±21.77 nm and 0.394±0.027, 98.0 % and 25.0 % for mean PS, PDI, EE and %NVP released at 48 h for Span® 80 niosomes. All characterisation was undertaken on the day of manufacture. In conclusion, a simple, cheap, rapid and precise method of manufacture of NVP niosomes was developed, validated and optimised using DoE and RSM and the product exhibited the target CQA.

Dry sliding wear performance of AA7075/MoS2 composite materials

Process optimization of microwave-assisted aqueous extraction of tannins and saponins from Malabar Spinach (Basella alba) leaves using ANN-GA and RSM methodology

The preset response surface methodology (RSM) designs are commonly used in a wide range of process and design optimization applications. Although they offer ease of implementation and good performance, they are not sufficiently adaptive to reduce the required number of experiments and thus are not cost effective for applications with high cost of experimentation. We propose an efficient adaptive sequential methodology based on optimal design and experiments ranking for response surface optimization (O‐ASRSM) for industrial experiments with high experimentation cost, limited experimental resources, and requiring high design optimization performance. The proposed approach combines the concepts from optimal design of experiments, nonlinear optimization, and RSM. By using the information gained from the previous experiments, O‐ASRSM designs the subsequent experiment by simultaneously reducing the region of interest and by identifying factor combinations for new experiments. Given a given response target, O‐ASRSM identifies the input factor combination in less number of experiments than the classical single‐shot RSM designs. We conducted extensive simulated experiments involving quadratic and nonlinear response functions. The results show that the O‐ASRSM method outperforms the popular central composite design, the Box–Behnken design, and the optimal designs and is competitive with other sequential response surface methods in the literature. Furthermore, results indicate that O‐ASRSM's performance is robust with respect to the increasing number of factors. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, response surface methodology (RSM) designs and an artificial neural network (ANN) are used to obtain the optimal conditions for the oxy-combustion of a corn–rape blend. The ignition temperature (Te) and burnout index (Df) were selected as the responses to be optimised, while the CO2/O2 molar ratio, the total flow, and the proportion of rape in the blend were chosen as the influencing factors. For the RSM designs, complete, Box–Behnken, and central composite designs were performed to assess the experimental results. By applying the RSM, it was found that the principal effects of the three factors were statistically significant to compute both responses. Only the interactions of the factors on Df were successfully described by the Box–Behnken model, while the complete design model was adequate to describe such interactions on both responses. The central composite design was found to be inadequate to describe the factor interactions. Nevertheless, the three methods predicted the optimal conditions properly, due to the cancellation of net positive and negative errors in the mathematical adjustment. The ANN presented the highest regression coefficient of all methods tested and needed only 20 experiments to reach the best predictions, compared with the 32 experiments needed by the best RSM method. Hence, the ANN was found to be the most efficient model, in terms of good prediction ability and a low resource requirement. Finally, the optimum point was found to be a CO2/O2 molar ratio of 3.3, a total flow of 108 mL/min, and 61% of rape in the biomass blend.

A statistical modeling-optimization approach for efficiency photocatalytic degradation of textile azo dye using cerium-doped mesoporous ZnO: A central composite design in response surface methodology

Application of the response surface method to optimize alkali activated cements based on low-reactivity ladle furnace slag

A multi-period inventory management model for perishable products is developed with simulation approach. A single-product scenario is modelled under continuous and periodic review policies, considering Weibull demand, non-null deterministic lead time, a combination of lost sales and backorders, fixed product shelf life, and service constraints. Eight inputs, including managerial aspects and product characteristics, and six outputs, i.e. the minimum cost and the optimal operating leverages of the two policies, are evaluated using Design of Experiments. Discrete-time simulation is used to determine, for each configuration, the optimal operating leverages, namely target stock level, reorder period, and base stock, which minimise the total cost under service constraints. The outcomes were then analyzed through Analysis of Variance and Response Surface Methodology, to assess the impact of each input parameter on the responses. Results show that the optimal operating leverages mainly depend on demand features, some unitary costs (order issuing in particular), shelf life, and lost sales fraction. The shelf life turns out to be significant only if lower than a threshold value, which varies with the shape factor of the demand distribution: for shelf life values greater than the threshold, the products could be managed as non-perishable from an inventory point of view.

This research work was carried out to explore the influence of extraction methods on the extraction of antioxidant from Asparagus racemosus roots. The microwave‐assisted extraction (MAE) method was more efficient as compared with continuous shaking extraction (CSE) method in terms of total phenolic content (TPC), total flavonoids content (TFC), 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), and total antioxidant capacity (TAC) content in the ethanolic extract of A. racemosus roots. Box–Behnken experimental design of response surface methodology (RSM) was used to optimize MAE method for three process parameters viz. solvent concentration (SC: 40%–90% v/v), extraction time (ET: 10–30 min), and microwave power (MP: 360–720 W). Model fitness of the experimental data was determined by multiple regression analysis and analysis of variance. The optimum targeted parameters were obtained as 65% v/v SC, 23 min ET, and 560 W MP, and the corresponding responses TPC, TFC, DPPH, FRAP, and TAC were found to be 9.377 mg GAE/g, 177.03 mg QE/g, 65.70%, 193.35 mM Fe+2/g, and 496.97 mg AAE/g with a desirability of 0.95. Fuzzy modeling with Mamdani‐type fuzzy inference was also utilized to obtain the most optimal conditions for specified process parameters and responses under the set of rules for process parameters and responses. The estimated results of responses by both RSM and fuzzy model showed good agreement with the experimental values.

This study experimentally investigated the parameters such as dynamic hardening, inertia track cross-sectional area, decoupler hardness, and fluid volume affecting the damping properties of passive hydraulic engine mounts. Two levels were determined for these parameters, and the full factorial design of experiment (DoE) was created. According to the DoE, hydraulic engine mounts with various design configurations were assembled and tested. Based on the test results, a response surface was created in the Hyperstudy software, and the optimal parameters were obtained via the surface for the desired hydraulic mount performance. Another mount close to the optimum design configuration was then analyzed by the Hyperstudy, and the analysis results were confirmed with the test results. It was observed that the experimental and analysis results were consistent. As a result, it has been demonstrated that the mount parameters can be decided with the response surface generated with the DoE. So, both time and cost advantages can be achieved by eliminating trial and error in production.

Gypsum building materials are widely used in the construction sector and their use is growing steadily. At the end of the life cycle of a building, these gypsum materials become demolition waste. Thus, the increased consumption of gypsum building materials leads to an increased amount of demolition waste. The recycling of such demolition waste is becoming more and more relevant and important, as the recycling of gypsum waste would save natural raw materials and reduce waste. The main objective of this study was to investigate the effect of waste cellulose fibers on the physical and mechanical properties of gypsum samples. First of all, gypsum waste board was shredded and the cellulose separated from the gypsum. The gypsum particles were then ground in a ball mill to a fine powder. Calcination was carried out at 160 °C for 1,50 hours of 1 kg of gypsum. Citric acid was used as a set retarder and the amount was 0,05 wt. % by weight of the gypsum powder. The amount of waste cellulose fibers was 1, 3, 5 and 10 wt. % and as reference sample used gypsum sample without cellulose. The density, flexural and compressive strength were determined for all the samples. The microstructure was assessed by optical microscopy and the acoustic insulation properties were determined. The results showed that it is possible to obtain 5,75 MPa of compressive strength and 1,2 MPa of flexural strength by including 3 and 5 wt. % of waste cellulose fibers. The results of the sound insulation performance of gypsum composites with different amounts of waste cellulose fibers show that the highest levels of sound insulation were achieved when the composite contained 3 % and 5 % by weight of waste cellulose fibers. The findings of the study indicate that the utilization of recycled gypsum can yield environmental benefits when compared with the use of natural gypsum.

This study investigates the development of Green Normal Concrete (GNC) by incorporating plastic waste aggregates (PWAs) and sawdust waste (SDW) as partial replacements for natural sand (NS). The research follows three experimental stages using Response Surface Methodology (RSM) and the Absolute Volume (AV) method to optimize compressive strength, workability, and density. Stage I establishes a normal concrete (NC) control mix based on British standards. Stage II examines the effects of replacing NS with gray and black ABS plastic granules (GABSR and BABSI), identifying the optimal mix (OPW) with 0.04% NS‐GABSR and 24.11% NS‐BABSI, achieving 20.50 MPa compressive strength, 25 mm workability, and 2276 kg/m3 density. Stage III introduces sawdust (SDF) as a fine aggregate replacement in OPW, with silica fume (DSF) and superplasticizer (SP) enhancing performance. The optimal mix (OPWSD), with 60.32% BABSI‐SDF and 20.01% NS‐SDF, achieves 25.10 MPa compressive strength, 124.21 mm workability, and 2055 kg/m3 density. The results highlight the feasibility of PWAs and SDW in concrete, emphasizing the need for further studies on long‐term durability and environmental impact.

Response surface methodology (RSM) based robustness optimization design is a fine technique to improve product quality, it is very suitable for solving multivariable robustness optimization problems. The rationale of response surface methodology is presented in this paper firstly, the central composite face centered design, which is one of three central composite design types, is used in RSM’s design of experiments (DOE), then the desirability function is applied to solve multiple design objectives so that the multiple responses can be changed into one function. After that, simulated annealing is used to solve overall desirability function. Finally, unmanned aerial vehicle electrostatic spray system is optimized using our method.

In this work, experiments were carried out in line with Design of Experiments (DOE) standards to assess the performance and emission features of 5% graphene nanoparticles added linseed biodiesel. The engine was operated with the blends of B10, B20, and B30 with 5% graphene nano additives (designated as B10G5, B20G5, and B30G5). To find the parameter's optimum values, the Desirability Function approach (DFA), Swarm Salp single objective, Multi Objective Bat algorithm (MOBA), Response surface methodology (RSM) and D-optimal design approach were employed. Advanced machine learning (ML) techniques were employed to anticipate these characteristics. It was found that B20G5 had a better brake thermal efficiency (BTE), when compared to the other samples (and around 11% higher than diesel fuel at full load). The emissions of Carbon monoxide (CO) and Hydrocarbon (HC) were lower for B20G5 blended fuel than for diesel (Around 23.52% lower than diesel). In comparison to Response surface methodology (RSM), the overall coefficient of determination (R2) value using Artificial Neural Network (ANN) for was high. As a result, it was revealed that the ANN was typically better than the RSM in forecasting the various factors affecting the engine performance. The optimum outcomes were achieved by single objective (Salp Swarm algorithm) and multi-objective algorithms. According to multi-objective algorithm, a B20G5 nano additive biodiesel mix at its maximum Brake power (BP) produced the highest value of BTE with the lowest Nitrogen Oxides (NOx) emissions. The comparison shows that B20G5 can be used easily without making any modifications to engines.

This research study focused on the various aspects of tribological phenomenon using design of experiments approach, results are analyzed and compared to evaluate the gear performance. Though lot of research work had been carried out on the hydrodynamic gear under static loads, till date no one consider the parameter say stiffness, damping co-efficient, squeeze film effects and the pressure distribution at various crank angle under dynamic load conditions and its effects on inertia forces of gear pump. It covers sinusoidal load, journal speed, face width and mass flow rate with respect to variations in oil film thickness. Frictional test rig was used to measure the frictional force and oil film thickness at various crank angles for every three minutes at an interval of every 15 degree crank angle. The response surface methodology (RSM) analysis shows appreciable increases in respect of performances in mass flow rate, face width, speed and loading against the Taguchi design of experiment results whereas the film thickness is almost same in both RSM and Taguchi design of experiment results. Particular attention is given in this research exertion to learn how the variation in gear dimensions influences the characteristics of the hydrodynamic gear pump. Keywords: Hydrodynamic Gear Pump, Oil Film Thickness, Mathematical Modeling, Taghuchi and RSM, Eddycurrent Cap Sensor.