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In the response surface methodology (RSM), the designed experiment helps create interfactor orthogonality and interpretable response models for the purpose of process and design optimization. However, along with the development of data-recording technology, observational data have emerged as an alternative to experimental data, and they contain potential information on design/process parameters (as factors) and product characteristics that are useful for RSM analysis. Recent studies in various fields have proposed modifications to the standard RSM procedures to adopt observational data and attain considerable results despite some limitations. This paper aims to explore various methods to incorporate observational data in the RSM through a systematic literature review. More than 400 papers were retrieved from the Scopus database, and 83 were selected and carefully reviewed. To adopt observational data, modifications to the procedures of RSM analysis include the design of the experiment (DoE), response modeling, and design/process optimization. The proposed approaches were then mapped to capture the sequence of the modified RSM analysis. The findings highlight the novelty of observational-data-based RSM (RSM-OD) for generating reproducible results involving the discussion of the treatments for observational data as an alternative to the DoE, the refinement of the RSM model to fit the data, and the adaptation of the optimization technique. Future potential research, such as the improvement of factor orthogonality and RSM model modifications, is also discussed.

This study is focused on the flotation of a cooper mineral.Chrysocolla is poor flotability, surface porous, high porosity, nonuniform property, so it has strong hydrophilic and difficult dissolution. XRD and SEM were used to detect the properties and surface morphology of chrysocolla. The paper make an experiment, it contain modified polymer adsorption - intermediate metal copper ion connection - collector adsorption testing program. The experiment can exchange mineral surface property which enhancing mineral flotation and hydrophobicity. With the conclusion, the results have a trend that increasing the agents can increase mineral recovery, then mineral recovery reach the stable trend. In the simulation of RSM, mineral recovery is based on 3 factors ammonium, xanthate and agent, those factors interact with each other, simulation find the main factor is agent. RSM response surface method has the function of optimizing test results, improving test efficiency, inputting test influence factors and results, and getting the best test factors and results through test simulation.

Synthesis of surface modified/multi-functional nanoparticles has become a vital research area of material science. In the present work, iron oxide (Fe3O4) nanoparticles prepared by solvo-thermal method were functionalized by polydopamine. The catechol groups of polydopamine at the surface of nanoparticles provided the sites for the attachment of Aspergillus terreus AH-F2 lipase through adsorption, Schiff base and Michael addition mechanisms. The strategy was revealed to be facile and efficacious, as lipase immobilized on magnetic nanoparticles grant the edge of ease in recovery with utilizing external magnet and reusability of lipase. Maximum activity of free lipase was estimated to be 18.32 U/mg/min while activity of Fe3O4-PDA-Lipase was 17.82 U/mg/min (showing 97.27% residual activity). The lipase immobilized on polydopamine coated iron oxide (Fe3O4_PDA_Lipase) revealed better adoptability towards higher levels of temperature/pH comparative to free lipase. The synthesized (Fe3O4_PDA_Lipase) catalyst was employed for the preparation of biodiesel from waste cooking oil by enzymatic transesterification. Five factors response surface methodology was adopted for optimizing reaction conditions. The highest yield of biodiesel (92%) was achieved at 10% Fe3O4_PDA_Lipase percentage concentration, 6:1 CH3OH to oil ratio, 37 °C temperature, 0.6% water content and 30 h of reaction time. The Fe3O4-PDA-Lipase activity was not very affected after first four cycles and retained 25.79% of its initial activity after seven cycles. The nanoparticles were characterized by FTIR (Fourier transfer infrared) Spectroscopy, XRD (X-ray diffraction) and TEM (transmission electron microscopy), grafting of polydopamine on nanoparticles was confirmed by FTIR and formation of biodiesel was evaluated by FTIR and GC-MS (gas chromatography-mass spectrometry) analysis.

Wire arc additive manufacturing (WAAM) has emerged as a promising technology for producing complex, large-scale components with high material deposition rates, reduced waste, and shorter lead times. This study focused on optimizing process parameters in cold metal transfer (CMT)-based WAAM to achieve superior bead geometry and enhanced microhardness in stainless steel 321. Welding current, travel speed, and shielding gas mixture ratio were selected as input parameters, whereas bead width, depth of penetration, diffusion area, and microhardness were the response variables. Using response surface methodology (RSM) with a central composite design, a regression model was developed to identify optimal process parameters. The optimal parameters are 150 A current, 4-5 mm/s travel speed, and 0-10% CO2 shielding gas, which produced favorable bead geometry and a microhardness of 180 HV, validating the RSM model’s predictive accuracy. Experimental validation confirmed the model’s accuracy in producing high-quality SS321 structures. Tensile testing validated mechanical performance, with yield strength 372.07-384.46 MPa, ultimate tensile strength (UTS) 590.19-598.96 MPa, and elongation 38% (90°) and 41% (0°), surpassing ASTM A240/A240M-20a standards. Scanning electron microscopy fractography revealed ductile failure with micro-voids and fine dimples, confirming a ductile mode of mechanical behavior. These findings demonstrate the reliability of RSM in optimizing CMT-WAAM processes and additive manufacturing applications.

Inconel 718, a hard-to-cut superalloy is reputed for having poor machining performance due to its low thermal conductivity. Consequently, the surface quality of the machined parts suffers. The surface roughness value must fall within the stringent limits to ensure the functional performance of the components used in aerospace and bioimplant applications. One doable way to enhance its machinability is the adequate dissipation of heat from the machining zone through efficient and ecofriendly cooling environment. With this perspective, an experimental and integrated green-response surface machining-based-evolutionary optimization (G-RSM-EO) approach is presented during this investigation. The results are compared with two base-line techniques: the traditional flooded approach with Hocut WS 8065 mineral oil, and the dry green approach. A Box-Behnken response surface methodology (RSM) is employed to design the milling tests considering three control parameters, i.e., cutting speed ( v s ), feed/flute ( f z ), and axial depth of cut ( a p ). These control parameters are used in the various experiments conducted during this research work. The parametric analysis is then accomplished through surface plots, and the analysis of variance (ANOVA) is presented to assess the effects of these control parameters. Afterwards, a multiple regression model is developed to identify the parametric relevance of v s , f z , and a p , with surface roughness (SR) as the response attribute. A residual analysis is performed to validate the statistical adequacy of the predicted model. Lastly, the surface roughness regression model is considered as the objective function of the particle swarm optimization (PSO) model to minimize the surface roughness of the machined parts. The optimized SR results are compared to the widely employed genetic algorithm (GA) and RSM-based desirability function approach (DF). The confirmatory machining tests proved that the integrated optimization approach with PSO being an evolutionary technique is more effective compared to GA and DF with respect to accuracy (0.05% error), adequacy, and processing time (3.19 min). Furthermore, the study reveals that the Mecagreen 450 biodegradable oil-enriched flooded strategy has significantly improved the milling of Inconel 718 in terms of eco-sustainability and productivity, i.e., 42.9% cost reduction in cutting fluid consumption and 73.5% improvement in surface quality compared to the traditional flooded approach and the dry green approach. Moreover, the G-RSM-EO approach presents a sustainable alternative by achieving a Ra of 0.3942 μm that is finer than a post-finishing operation used to produce close tolerance reliable components for aerospace industry.

The preparation of wheat straw biochar (MWSB) was optimized using response surface methodology (RSM) with a Box-Behnken Design (BBD) to maximize Cr(VI) removal. Parameters assessed were wheat straw particle size, KOH modifier concentration, and pyrolysis temperature. Optimal conditions (0.1 mm particle size, 3 mol/L KOH, 494 °C pyrolysis) yielded 86.5% Cr(VI) removal efficiency. Adsorption kinetics followed the pseudo-second-order model, and isotherm data fitted the Langmuir model, indicating monolayer adsorption limited by site density. The Langmuir model gave a maximum adsorption capacity (Qmax) of 105.28 mg/g at 25 °C. MWSB was characterized using SEM-EDS, FTIR, Raman spectroscopy, and XPS. The optimized MWSB preparation significantly enhanced the efficacy and feasibility of wheat straw in environmental applications, particularly for Cr(VI) removal.

Precision turning of CK45 steel for calibration devices requires stringent control of surface characteristics, geometrical accuracy, and tool wear. This paper introduces a hybrid intelligent modeling and optimization framework that combines Response Surface Methodology (RSM) with Artificial Neural Networks (ANN) to simultaneously forecast machining efficiency and correlate it with microstructural evolution. Experiments were carried out via cubic boron nitride (CBN) tools under a central composite design, considering spindle speed (N), feed rate (F), depth of cut, (D) and tool nose radius (R) as key process parameters. The effects of these variables on material removal rate (MRR), tool wear rate (TWR), surface roughness parameters (Ra and R max ), roundness error (OR), and hardness (H) were systematically analyzed. While RSM regression models provided initial predictive capability, their integration with ANN significantly enhanced accuracy, yielding prediction errors below 7% for all responses. Multi-objective optimization identified machining conditions that minimized tool wear (1.23 × 10⁻⁵ g/min) while improving surface quality and dimensional precision. Microstructural analysis further revealed the formation of refined and homogeneous dendritic structures with characteristic sizes ranging from 10.86 to 17.44 μm under optimized conditions. The proposed hybrid intelligent RSM–ANN framework offers a robust and reliable approach for precision turning optimization and enables new insights into the linkage between machining parameters and microstructural characteristics of CK45 steel.

Fused deposition modeling (FDM) is gaining distinct advantages because of its ability to fabricate the 3D physical prototypes without the restrictions of geometric complexities, while when it comes to accuracy and efficiency, the advantages of FDM is not distinct, and so how to improve them is worthy of study. Focusing on process parameter optimization, such parameters as line width compensation, extrusion velocity, filling velocity, and layer thickness are selected as control factors, input variables, and dimensional error, warp deformation, and built time are selected as output responses, evaluation indexes. Experiment design is assigned according to uniform experiment design, and then the three output responses are converted with fuzzy inference system to a single comprehensive response. The relation between the comprehensive response and the four input variables is derived with second-order response surface methodology, the correctness of which is further validated with artificial neural network. Fitness function is created using penalty function and is solved with genetic algorithm toolbox in Matlab software. With confirmation test, the results are obtained preferring to the results of the experiment 1 with the best comprehensive response among the 17 experiment runs, which confirms that the proposed approach in this study can effectively improve accuracy and efficiency in the FDM process.

Cadmium is one of the most hazardous metals in the environment, even when present at very low concentrations. This study reports the systematic development of Kenaf fiber biochar as an adsorbent for the removal of cadmium (Cd) (II) ions from water. The adsorbent development was aided by an optimization tool. Activated biochar was prepared using the physicochemical activation method, consisting of pre-impregnation with NaOH and nitrogen (N[sub.2]) pyrolysis. The influence of the preparation parameters—namely, chemical impregnation (NaOH: KF), pyrolysis temperature, and pyrolysis time on biochar yield, removal rate, and the adsorption capacity of Cd (II) ions—was investigated. From the experimental data, some quadratic correlation models were developed according to the central composite design. All models demonstrated a good fit with the experimental data. The experimental results revealed that the pyrolysis temperature and heating time were the main factors that affected the yield of biochar and had a positive effect on the Cd (II) ions’ removal rate and adsorption capacity. The impregnation ratio also showed a positive effect on the specific surface area of the biochar, removal rate, and adsorption capacity of cadmium, with a negligible effect on the biochar yield. The optimal biochar-based adsorbent was obtained under the following conditions: 550 °C of pyrolysis temperature, 180 min of heating time, and a 1:1 NaOH impregnation ratio. The optimum adsorbent showed 28.60% biochar yield, 69.82% Cd (II) ions removal, 23.48 mg/g of adsorption capacity, and 160.44 m[sup.2]/g of biochar-specific area.