RSM Model Research Articles

Azadirachta indica seed oil epoxidation process using carbonized melon seed peel catalyst; genetic algorithm coupled artificial neural network approach

The study used ANN-GA and RSM to predict the best process parameters for generating epoxide from Azadirachta indica seed oil (AISO). This procedure used carbonized sulphonated melon seed peel catalyst. FTIR, SEM, XRD, BET, and XRF measurements confirm the -SO3H group's attachment to the solid catalyst. The dependant variable was relative conversion to oxirane (RCO), while the independent parameters were catalyst dosage (0.6, 1.2, 1.8 wt %), time (4, 6, 8 h), and temperature (50°C, 60°C, 70°C). The ANN was evaluated using 11 backpropagation (BP) methods. Each method was examined with three input layer neurons for catalyst dosage, duration, and temperature. Ten neurons were in the hidden layer and one was in the output layer signifying RCO. The AISO epoxidation process forecast was most accurate using Bayesian regularisation. Simulated RSM and ANN models were built using experimental and algorithmic designs. The 3D plots showed that process parameters significantly affected RCO. R2 and MSE were used to evaluate model performance. For process forecasting, the ANN model (R2=0.9999, MSE=2.3404E-13) outperforms the RSM model (R2=0.9979, MSE=0.4688). Under the best RSM circumstances, RCO yield was 78.03 %. Additionally, the ANN and ANN-GA yielded 85.84 % and 92.51 %, respectively at optimal conditions of 0.6 wt % catalyst, 50°C temperature, and 6 h reaction time. However, all techniques optimized AISO and matched experimental results (RCO-77.41 %). FT-IR and GCMS characterizations of epoxy AISO corroborated the oxirane ring's attachment. The results show that ANN-GA is a reliable method for modelling and optimizing AISO epoxide production utilizing CSMSPC, encouraging sustainable development.

Performance optimization for an optimal operating condition for a shell and heat exchanger using a multi-objective genetic algorithm approach.

In this study, shell and heat exchangers are optimized using an integrated optimization framework. In this research, A structured Design of Experiments (DOE) comprising 16 trials was first conducted to systematically determine the essential parameters, including mass flow rates (mh, mc), temperatures (T1, t1, T2, t2), and heat transfer coefficients (€, TR, U). By identifying the first four principal components, PCA was able to determine 87.7% of the variance, thereby reducing the dimensionality of the problem. Performance-related aspects of the system are the focus of this approach. Key outcomes (€, TR, U) were predicted by 99% R-squared using the RSM models. Multiple factors, such as the mass flow rate and inlet temperature, were considered during the design process. The maximizing efficiency, thermal resistance, and utility were achieved by considering these factors. By using genetic algorithms, Pareto front solutions that meet the requirements of decision-makers can be found. The combination of the shell and tube heat exchangers produced better results than expected. Engineering and designers can gain practical insight into the mass flow rate, temperature, and key responses (€, TR, U) if they quantify improvements in these factors. Despite the importance of this study, it has several potential limitations, including specific experimental conditions and the need to validate it in other situations as well. Future research could investigate other factors that influence system performance. A holistic optimization framework can improve the design and engineering of heat exchangers in the future. As a result of the study, a foundation for innovative advancements in the field has been laid with tangible improvements. The study exceeded expectations by optimizing shell and heat exchanger systems using an integrated approach, thereby contributing significantly to the advancement of the field.

Evaluation of corrosion potential in near-shore piles and analysis through RSM modelling

Corrosion is a significant problem in marine environments that ultimately leads to structural failure and economic losses. The assessment of the conditions of concrete piles is an essential part of corrosion prevention measures in marine environments. This research investigated the corrosion potential of concrete marine piles using a half-cell potentiometer. Following this, the observed potential values are subjected to RSM modeling. Approximately ten piles were chosen in Chennai Fishing Harbor, and the corrosion potential values along the depth of the piles in three different zones, namely, the atmospheric, splash and submerged zones, were measured at three-month intervals. Based on the visual inspection, the piles were damaged, and minor cracks developed on the surfaces of the concrete due to the dynamic impact of wave action, specifically in the splash zone. From the results obtained in the field for the chosen piles in the fishing harbor, the corrosion potential values are in the range of −361.45 mV to −527.57 mV in the atmospheric zone, − 520.67 mV to −784.85 mV in the splash zone and– 401.23 mV to—631.28 mV in the submerged zone. RSM was noted to be the most accurate predictor with the highest R2 and least errors. The RSM model captured the variability of the data according to the R2 threshold (R2 > 0.9855). The splash zones have higher corrosion potentials and are more prone to corrosion than the other zones.

Numerical analysis of cyclone separators with unique Dipleg structures at different Dipleg-to-dustbin ratios

This study investigates the influence of various configurations of dipleg and dustbin structures on cyclone separator performance. For comparative analysis, the inverted cone dipleg which reported has the highest collection efficiency was employed, and six models were developed with varying dipleg-to-dustbin ratios while maintaining a constant total height. Unsteady simulations were conducted using the RSM model and the Eulerian-Lagrangian approach to analyze the flow dynamics and particle migration behaviors in cyclone separators. The increase in dipleg length significantly influenced flow behavior in both the dipleg and dustbin regions, initially resulting in increased flow complexity before transitioning to smoother and more regular patterns. Analysis of vortex frequency indicated a weakening back-mixing effect with increased dipleg length, further underscoring the impact of this parameter on cyclone performance. Quality factors under different particle size ranges are proposed to compare the separation efficiency and pressure drop of different models, providing a reference for the selection of cyclone in different application scenarios.

Mathematical modelling for predicting performance and emissions of a diesel engine fuelled with palm kernel biodiesel–diesel-diethyl carbonate fuel blends

The search for environmentally friendly and sustainable alternatives to conventional diesel fuels has prompted investigations into blends of biodiesel and oxygenated additives like diethyl carbonate. This study focuses on developing mathematical models to predict the performance and emissions of a single-cylinder diesel engine running on palm kernel biodiesel blended with varying proportions of diethyl carbonate. Response Surface Methodology with a general factorial design was employed to create mathematical models for brake thermal efficiency, brake-specific fuel consumption, hydrocarbon, carbon monoxide, and oxides of nitrogen emissions. The two input variables were engine load (five levels) and diethyl carbonate percentage in the fuel blend (five levels). Second-order models were developed, and model fit was evaluated using analysis of variance (ANOVA) at 95% confidence. The developed RSM models showed excellent fit with high R-squared values close to unity, satisfying the model adequacy criteria. ANOVA revealed the models to be statistically significant predictors of the response variables based on load and diethyl carbonate percentage. The optimum operating condition for best performance and emissions balance was found to be 71% load and 10% diethyl carbonate inclusion. The study emphasises the importance of alternative fuels in mitigating environmental pollution and reducing dependence on fossil fuels.

Effect of Nano-TiO2 and Polypropylene Fiber on Mechanical Properties and Durability of Recycled Aggregate Concrete

In order to promote the engineering application of recycled concrete, the effects of PPF and nano-TiO2 dioxide on the mechanical properties and durability of recycled concrete were studied.Polypropylene fiber recycled concrete(PRAC) and nano-TiO2 recycled concrete(TRAC) were prepared by adding different volume contents of PPF and nano-TiO2. The experimental findings demonstrated that the PPF and nano-TiO2 improved the splitting tensile strength of RAC better than the compressive strength. When the volume content of nano-TiO2. and PPF is 0.8% and 1.0%, respectively, the corresponding splitting tensile strength of concrete reaches the maximum value(3.4 and 3.7 MPa). The contribution rates of nano-TiO2 and PPF with different volume contents to the mechanical properties of RAC have optimal values, which are 0.4 and 1.0%, respectively. The incorporation of nano-TiO2 and PPF can effectively inhibit the loss of RAC mass and the generation of pores under freeze–thaw conditions, and slow down the decrease of dynamic elastic modulus. When the volume content of PPF is 1.0% and the volume content of nano-TiO2 is 0.4%, the protection effect on the internal structure of RAC is better, and its carbon resistance is better. The results of RSM model analysis and prediction show that both PPF and nano-TiO2 can be used as admixture materials to improve the mechanical properties and durability of RAC, and the comprehensive improvement effect of PPF on RAC performance is better than that of nano-TiO2.

Bioprospective decolourization of reactive azo dyes at pilot scale by a developed bacterial consortium using the RSM and CCD model

Bioprospective decolourization of reactive azo dyes at pilot scale by a developed bacterial consortium using the RSM and CCD model

Optimisation of POME biodiesel with isobutanol additive to cater UN sustainable development goal on affordable and clean energy

Biodiesel is a suitable alternative to solve global pollution and declining non-renewable resources. This aligns with the sustainable climate goals and policies to cater to SDG 7. Sustainable technology and solutions have to be fostered. The higher blends of biodiesel will result in engine performance and emissions degradation. Therefore, this research aims to improve the engine performance and emissions of diesel engines operating with B20 POME biodiesel and isobutanol. The Central Composite Design (CCD) model was used to construct the RSM model to determine the optimised condition of the engine testing. Engine performance and emissions of diesel engines were tested on the Yanmar TF120M at 50 % load. Six fuel samples, diesel, B20 Palm Oil Methyl Ester (POME) biodiesel blended fuel, and another four B20 POME biodiesel blends added with 5–20 % in volume percentage of isobutanol, were tested. The optimised RSM obtained a desirability of 0.7. The optimal conditions for the engine testing were at 1868 rpm and a 10 % isobutanol additive percentage. The brake power, BSFC, and BTE of the B95IBU5 and B90IBU10 show an improvement compared to the B20. The exhaust emission shows the lowest CO emission for B90IBU10 at 50 % load. The lowest NOx emission was obtained by B95IBU5 and B90IBU10, with an improvement of 1.8 % and 2.4 %, respectively. Therefore, from this study, it can be concluded that a lower percentage of isobutanol additive of 5–10 % is a promising additive for biodiesel blended fuels.

Predicting the impact of adding metakaolin on the splitting strength of concrete using ensemble ML classification and symbolic regression techniques –a comparative study

The mechanical characteristics of concrete are crucial factors in structural design standards especially in concrete technology. Employing reliable prediction models for concrete’s mechanical properties can reduce the number of necessary laboratory trials, checks and experiments to obtain valuable representative design data, thus saving both time and resources. Metakaolin (MK) is commonly utilized as a supplementary replacement for Portland cement in sustainable concrete production due to its technical and environmental benefits towards net-zero goals of the United Nations Sustainable Development Goals (UNSDGs). In this research work, 204 data entries from concrete mixes produced with the addition of metakaolin (MK) were collected and analyzed using eight (8) ensemble machine learning tools and one (1) symbolic regression technique. The application of multiple machine learning protocols such as the ensemble group and the symbolic regression techniques have not been presented in any previous research work on the modeling of splitting tensile strength of MK mixed concrete. The data was partitioned and applied according to standard conditions. Lastly, some selected performance evaluation indices were used to test the models’ accuracy in predicting the splitting strength (Fsp) of the studied MK-mixed concrete. At the end, results show that the k-nearest neighbor (KNN) outperformed the other techniques in the ensemble group with the following indices; SSE of 4% and 1%, MAE of 0.1 and 0.2 MPa, MSE of 0, RMSE of 0.1 and 0.2 MPa, Error of 0.04% and 0.04%, Accuracy of 0.96 and 0.96 and R2 of 0.98 and 0.98 for the training and validation models, respectively. This is followed closely by the support vector machine (SVM) with the following indices; SSE of 7% and 3%, MAE of 0.2 and 0.2 MPa, MSE of 0.0 and 0.1 MPa, RMSE of 0.2 and 0.3 MPa, Error of 0.05% and 0.06%, Accuracy of 0.95 and 0.94, and R2 of 0.96 and 0.95, for the training and validation models, respectively. The third model in the superiority rank is the CN2 with the following performance indices; SSE of 15% and 4%, MAE of 0.2 and 0.2 MPa, MSE of 0.1 and 0.1 MPa, RMSE of 0.3 and 0.3 MPa, Error of 0.08% and 0.07%, Accuracy of 0.92 and 0.93 and R2 of 0.92 and 0.93, for the training and validation models, respectively. These models outperformed the models utilized on the MK-mixed concrete found in the literature, therefore are the better decisive modes for the prediction of the splitting strength (Fsp) of the studied MK-mixed concrete with 204 mix data entries. Conversely, the NB and SGD produced unacceptable model performances, however, this is true for the modeled database collected for the MK-mixed Fsp. The RSM model also produced superior performance with an accuracy of over 95% and adequate precision of more than 27. Overall, the KNN, SVM, CN2 and RSM have shown to possess the potential to predict the MK-mixed Fsp for structural concrete designs and production.

Optimizing injection molding parameters to minimize and prediction potential for flashing defects

The injection molding process is a manufacturing process that can produce products in a short time in large quantities, in the injection molding process the factor of setting process parameters plays a significant role in product quality, so it requires special treatment. The purpose of this study is to find the optimal parameters in the injection moulding process of yogurth container lid with polypropylene material, so that the process can reduce the incidence of flashing defects that result in the emergence of initial waste in the industrial environment. The method used in this research is to create a Response Surface Methodology Box-Behnken Design (RSM - BBD) optimization model and an Artificial Neural Network (ANN) model approach in analyzing optimal parameters and predicting the appearance of flashing defects in a designed cycle. The results obtained from this research are the optimal parameters from the RSM and ANN model recommendations, namely the clamping force setting of 70 tons, holding time 0.1 seconds, and holding pressure. The ANN model provides the highest level of prediction accuracy with an R2 value of 100% and a prediction error rate of 7.9689E-09. In comparison, the RSM model obtains a prediction accuracy level with an R2 of 71% with an error rate of 0.24315.

Comparing Taguchi-based RSM and ANN for Shredder Blade Geometrical Parameter Optimization

In this study, a Taguchi-based RSM in conjunction with an ANN model was utilized to ascertain optimal geometric parameters for the shredding blade employed in a plastic bottle shredder. The shredding process is pivotal in plastic recycling, involving the reduction of waste plastic into smaller fragments to facilitate subsequent transportation and processing. Despite existing research on plastic shredders, further investigations are warranted to optimize shredding blade design. Consequently, a numerical analysis, providing an in-depth insight into understanding the shredder parameters to elucidate the influence of geometric factors was conducted. Subsequent validation was carried out using experimental designs prescribed by the Taguchi-based RSM and ANN models. Both models were then evaluated based on predictive effectiveness and error against simulation data. The predictive outcomes presented that the ANN model resulted in better prediction capacity and lower prediction error than the RSM model, 0.16197 μm and 0.15567 μm, while the numerical validation value was 0.162 μm. Both the original and optimal blades were fabricated and utilized for experiments, illustrating lower wear after measurement using a microscope from ICamScope®. As a result, it is evident from this inquiry that this methodology presents a viable avenue for enhancing the efficiency of plastic recycling machinery and broader industrial applications.

Effect of Different Outlet Structure of Axial Flow Cyclone Separator in Drying Tower

The axial flow cyclone separator used in drying tower is characterized by the diameter of the dust outlet at the tail is larger than that of the cylinder, and the dust removal efficiency is increased by a series dust outlet. The numerical analysis of its separation characteristics is carried out by using the RSM model of CFD. The results show that the separation efficiency can be increased by appropriately increasing the diameter of the dust outlet, and the separation efficiency will not change when the diameter of the dust outlet increases to a certain value. When the insertion depth of the air outlet is reduced from 150mm to 100mm, the average separation efficiency of particles with a diameter of 15-30um is reduced by 4.5%. When the insertion depth of the air outlet is greater than the width of the dust outlet, the insertion depth has no effect on the dust removal efficiency. The tandem dust outlet and parallel dust outlet have the same separation effect on small particles, and the gas flow rate is equivalent to twice the gas flow rate of a single dust outlet. The average particle separation efficiency of 1 - 30um is increased by 16.5% compared with that of a single dust outlet. The flow velocity of the tandem dust outlet is higher than that of the parallel dust outlet.

Numerical and experimental investigation of performance and emission characteristics of hydrogen enrichment with prosopis juliflora biodiesel in CI engine using RSM and ANN optimization

IC engines need renewable fuel due to environmental degradation and fossil fuel depletion. Biodiesel was a popular alternative fuel. However, hydrogen has zero emissions and a higher auto ignition temperature. This study examines diesel engine operation with hydrogen-enriched juliflora biodiesel/diesel blend (B20). The 4-Stroke, direct injection diesel engine (KIRLOSKAR-AV1) produces 5.2 kW at 1500rpm. A B20 compound was injected into the combustion cylinder via the intake manifold during testing. Hydrogen was supplied through the intake manifold. Diesel (D0), B20, and B20 with hydrogen are tested on 6 lpm, 9 lpm, and 12 lpm respectively. Increase in hydrogen concentration with B20 blend will increased brake thermal efficiency. The mixture B20 at a flow rate of 12 L per minute increased the BTE by 27.51%, which is a 4.88% improvement compared to the original B20. Similarly, this concentration exhibit an 18.75% reduced Brake Specific Fuel Consumption (BSFC) compared to the B20 combination. CO and HC emissions decreases and increased in NOx emissions while increasing H2 flow. Further, to develop the RSM and ANN predicted model for optimization of CI engine characteristics responses. The RSM model analyzed the lower mean square error (MSE) and actual relative error (ARE) for the engine responses compared to the ANN model.

Applications of integrated response surface methodology statistic techniques and artificial neural network‐based machine learning to optimize residual chlorine production and energy consumption

AbstractA multifactor interaction study was performed using the combined response surface methodology and an artificial neural network on the operational parameters and their influence on residual chlorine production. The operating variables, sodium chloride concentration, electrical potential, electrolysis time, and electrode gap, were evaluated over the response, residual chlorine and energy consumption. The results indicated that the optimum value for residual chlorine was 2450 mg/L achieved at an electrical potential of 8.8 V for 25 min in the presence of 25 g/L of sodium chloride and an electrode distance of 1 cm, and the optimum corresponding energy consumption was measured at 21.76 kWh/L. The study reveals that electric potential, sodium chloride concentration, and electrolysis time positively influence residual chlorine production. ANN models showed superior prediction ability compared with RSM models. This suggests electrolysis can be used for active chlorine production from saline solutions, potentially for industrial applications and water disinfection.

Thermodynamic analysis and examining the effects of parameters in BSR-HDH system using response surface methodology

1) BackgroundIn this study, biogas steam reforming (BSR) coupled with a humidification dehumidification unit (HDH) was proposed and the novel thermodynamic analysis interaction effects on energy efficiency and exergy efficiency via the design of experiments was used. 2) MethodsComprehensive thermodynamic modelling has been performed using EES software. Thus, by utilizing the thermodynamic analysis of the combined system by EES software and transferring the experiments based on the central composite design for the input parameters (the inlet temperature of the dehumidifier, humidifier and dehumidifier circulated mass flow rate and the desalination heater inlet temperature) extracted by the design expert software, the results obtained from the EES provides responses that shows the impact of the interaction of the input parameters. In RSM model, the central composite design (CCD) is employed in the experimental design. 3) Significant FindingsFrom the thermodynamic outlet results, the energy efficiency, exergy efficiency, hydrogen mass flow rate and freshwater mass flow rate of the system are obtained 82.39% and 72.65%, 0.1071 kg/s and 0.211 kg/s, respectively. R2 values in energy and exergy efficiency responses were calculated 99.99% and 99.97%, respectively that shows the model has a good accuracy. The optimum points for parameters of the inlet temperature of dehumidifier (T14), humidifier and dehumidifier circulated mass flow rate (m19) and the desalination heater inlet temperature (T10) and also responses of energy efficiency and exergy efficiency are obtained 310 K, 8 kg/s, 450 K, 0.9051% and 0.7313%, respectively. Also, the variables of the inlet temperature of dehumidifier and humidifier and dehumidifier circulated mass flow rate (m19) have more interaction with each other based on their slope changes in the upper and lower ranges.

H2S Removal from Biogas using Steel Wool (Fe2O3) Adsorption Combined with Water Absorption: Experimental and Modelling via RSM

Hydrogen sulfi de (H2S) is a hazardous gas commonly found in biogas systems; it causes problems in biogas utilization because H2S is highly corrosive to engines, gas storage tanks, and generators. The purpose of this research was to study the biogas upgrading by removing H2S using SCM440 type steel wool (Fe2O3) adsorption combined with water absorption. The biogas upgrading column was operated at a biogas fl ow rate of 0.5–3 l/min, with 10–50 g of treated Fe2O3 adsorbent packed in the middle of the column, and 60–300 ml of water contained at the bottom of the column. The optimal conditions obtained by the RSM for maximum H2S removal were 1 l/min gas fl ow rate, 40 g Fe2O3 and 240 ml water, can be H2S removal of 50.79 g/m3 biogas. Statistical, ANOVA analysis showed that Fe2O3 was the most signifi cant factor followed by water and gas fl ow rate. RSM models showed acceptable prediction of experimental data with maximum R2 value of 0.9804. The Fe2O3 regeneration conditions were examined at air fl ow rates of 1–4 l/min, regeneration times of 1–4 h, and 1–4 repeated uses for H2S removal. The optimum condition was found at a 2 l/min air fl ow rate for 2 h, resulting in a maximum H2S removal of 97.39%. Hence, the Fe2O3 adsorbent combined with water absorption technique has high potential for environmentally friendly and low-cost removal of H2S from biogas at an industrial level.

Exploring effects of carbon, nitrogen, and phosphorus on greywater treatment by polyculture microalgae using response surface methodology and machine learning

The microalgae-based wastewater treatment is a promising technique that contribute to achieving sustainable development goals (SDGs), such as SDG-6, “Clean Water and Sanitation”. However, it is strongly influenced by the initial composition of wastewater. In this study, the impact of initial organics and nutrient concentration on the removal of total organic carbon (TOC), total carbon (TC), ammonium (NH4+), total nitrogen (TN), and phosphate (PO43−) from greywater using native polyculture microalgae was explored. Response surface methodology was employed along with two machine learning approaches, AdaBoost and XGBoost, to evaluate the interactions among three main factors: TOC, NH4+, and PO43−, and their effects on treatment efficiency. The C/N ratios for achieving maximum TOC and TC removal efficiency of 99.2% and 97.7% were determined to be 10.3, and 65.4–73.6, respectively. Notably, the N/P ratio did not significantly affect their removal. The highest NH4+ removal efficiency, reaching 96.2%, was attained at C/N ratios of 4.3, 24.0, 38.2, and 212.9, coupled with N/P ratios of 0.3, 2.6, and 23.4. Highest TN removal efficiency of 77.2% was achieved at C/N and N/P ratios of 12.2 and 2.0, respectively. Highest PO43− removal of 78.8% was obtained at N/P ratio 12.8. However, C/N ratio did not affect the removal efficiency. Maintaining these specified C/N and N/P ratios in the influent greywater would ensure that the treated greywater meets the required standards for various reuse applications, including flushing, groundwater recharge, and surface water discharge. The integration of RSM with AdaBoost and XGBoost provided accurate predictions of removal efficiencies. For all the models, XGBoost had the highest R2, and lowest MAE and MSE values. The cross validation of RSM models with AdaBoost and XGBoost further reinforced the reliability of these models in predicting treatment outcomes.

Performance and emission characteristics of a diesel engine fuelled with Mesua ferrea biodiesel with chromium oxide (Cr2O3) nanoparticles: Experimental approach and response surface methodology

This study investigates the effects of using nano-fuelled diesel engines, including Mesua ferrea biodiesel and chromium oxide (Cr2O3) nanoparticles. The RSM model was created to make accurate predictions of the operating parameters. The amount of Cr2O3 nanoparticles was set at 60, 80, and 100 mg/L and an equal amount of surfactant and dispersant was added to the nanoparticles. The test was carried out under different compression ratios and load conditions. The parameters evaluated were the engine loads (3, 6, 9, and 12 kgf), fuel samples, and compression ratios (16.5:1, 17.5:1, and 18.5:1) as inputs and the parameters BTE, BSFC, CO, UHC, NOx, and smoke opacity as outputs. The BD20 + Cr2O3 80 mg/L + DSP 80 mg/L fuel sample resulted in a significant improvement in BTE by 16.58 % and a reduction in BSFC by 0.58 % compared to the other samples, while CO concentration decreased by 31.85 %, UHC concentration by 22.23 %, NOx concentration by 6.16 % and smoke opacity by 62.61 % at CR 18.5:1 and full load. The correlation coefficient (R2), which was determined using response surface methods and artificial neural networks, was between 0.96 and 0.98 for all output parameters. The observed range of values demonstrates a strong correlation between the experimental data and the predicted results.

Application of response surface methodology and neural networks in pyramid solar still for seawater desalination: An optimization and prediction strategy

<p>Potable water is essential in various aspects of daily life. Converting brackish water to drinkable water using traditional methods is costly and has environmental impacts. Solar energy is preferred over fossil fuels and other energy sources due to its cost and environmental benefits. Solar stills are increasingly popular due to the growing need for drinkable water, but their performance requires improvement. The study examines the pyramid solar still (PSS) productivity using different operating parameters, such as solar intensity (350-950 W/m2), water inlet temperature (30-50°C), water depth (4-8 cm), and insulation thickness (0.05-0.15m). The response surface technique (RSM) was used to examine the performance of the still under different operating conditions. The RSM approach optimized the process for maximum output and reduced testing time and effort. To verify the optimum response derived from RSM, the artificial neural networks (ANN) model was implemented. The comparative studies of the ANN and RSM models demonstrates a strong correlation with the outcomes achieved in maximizing the productivity of PSS. It found that the ideal values for solar intensity, water intake temperature, water depth, and insulation thickness are 950 W/m2, 50°C, 4 cm, and 0.15 m, respectively. These parameters contribute to the production of 2.585 kg/m2 of distillate.</p>

Particulate filtration by cryogenic condensation using a high efficiency Stairmand cyclone separator

This paper describes the synergy of a high-efficiency Stairmand cyclonic separator and the phenomenon of condensation generated by the low temperatures of the mixture of liquid nitrogen and ethanol, to increase the separation efficiency on particles with diameters of 1 μm, 45 μm, 75 μm, 150 μm and 250 μm. The cyclone separator adds the phenomenon of condensation, generating a film of liquid water on its internal walls. The film of liquid water causes a loss of kinetic energy in the particles, reducing their speed and becoming stuck. Al2O3 particles and pozzolana rock particles are used to perform the particle separation experimental tests. A computational fluid dynamics analysis using the second order RSM model is incorporated to describe the fluid dynamic behavior of the particles within the separator cyclone. The results in computational fluid dynamics show the agglomeration of larger diameter particles near the internal walls. Using condensation as a phase change generated by cryogenic mixing results in an overall particle separation efficiency of 76.00 %, standing for an increment of 70.14 % in comparison to a conventional high-efficiency Stairmand separator cyclone.Technological innovation: Particles, filtration, separation, condensation, cryogenic.