scholarly journals Exploring the Exhaust Emission and Efficiency of Algal Biodiesel Powered Compression Ignition Engine: Application of Box–Behnken and Desirability Based Multi-Objective Response Surface Methodology

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5968
Author(s):  
Prabhakar Sharma ◽  
Ajay Chhillar ◽  
Zafar Said ◽  
Saim Memon
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Fossil Fuels ◽  
Objective Response ◽  
Interaction Model ◽  
Operating Conditions ◽  
Exhaust Emission ◽  
Operating Parameters ◽  
Compression Ignition Engine ◽  
Multi Objective

Sustainable Development Goals were established by the United Nations General Assembly to ensure that everyone has access to clean, affordable, and sustainable energy. Third-generation biodiesel derived from algae sources can be a feasible option in tackling climate change caused by fossil fuels as it has no impact on the human food supply chain. In this paper, the combustion and emission characteristics of Azolla Pinnata oil biodiesel-diesel blends are investigated. The multi-objective response surface methodology (MORSM) with Box–Behnken design is employed to decrease the number of trials to conserve finite resources in terms of human labor, time, and cost. MORSM was used in this study to investigate the interaction, model prediction, and optimization of the operating parameters of algae biodiesel-powered diesel engines to obtain the best performance with the least emission. For engine output prediction, a prognostic model is developed. Engine operating parameters are optimized using the desirability technique, with the best efficiency and lowest emission as the criteria. The results show Theil’s uncertainty for the model’s predictive capability (Theil’s U2) to be between 0.0449 and 0.1804. The Nash–Sutcliffe efficiency is validated to be excellent between 0.965 and 0.9988, whilst the mean absolute percentage deviation is less than 4.4%. The optimized engine operating conditions achieved are 81.2% of engine load, 17.5 of compression ratio, and 10% of biodiesel blending ratio. The proposed MORSM-based technique’s dependability and robustness validate the experimental methods.

scholarly journals Fixed bed utilization for the isolation of xylene vapor: Kinetics and optimization using response surface methodology and artificial neural network

2020 ◽  
Vol 26 (2) ◽  
pp. 200105-0
Author(s):  
Kaushal Naresh Gupta ◽  
Rahul Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Operating Parameters ◽  
Bed Height ◽  
Artificial Neural

This paper discusses the isolation of xylene vapor through adsorption using granular activated carbon as an adsorbent. The operating parameters investigated were bed height, inlet xylene concentration and flow rate, their influence on the percentage utilization of the adsorbent bed up to the breakthrough was found out. Mathematical modeling of experimental data was then performed by employing a response surface methodology (RSM) technique to obtain a set of optimum operating conditions to achieve maximum percentage utilization of bed till breakthrough. A fairly high value of R2 (0.993) asserted the proposed polynomial equation’s validity. ANOVA results indicated the model to be highly significant with respect to operating parameters studied. A maximum of 76.1% utilization of adsorbent bed was found out at a bed height of 0.025 m, inlet xylene concentration of 6,200 ppm and a gas flow rate of 25 mL.min-1. Furthermore, the artificial neural network (ANN) was also employed to compute the percentage utilization of the adsorbent bed. A comparison between RSM and ANN divulged the performance of the latter (R2 = 0.99907) to be slightly better. Out of various kinetic models studied, the Yoon-Nelson model established its appropriateness in anticipating the breakthrough curves.

scholarly journals EXPERIMENTAL AND MODELING STUDIES OF VIETNAM LEMONGRASS ESSENTIAL OIL EXTRACTION PROCESS USING RESPONSE SURFACE METHODOLOGY

2018 ◽  
Vol 56 (2A) ◽  
pp. 11-16
Author(s):  
Nguyen Trung Dung
Keyword(s):  
Response Surface Methodology ◽  
Moisture Content ◽  
Response Surface ◽  
Steam Distillation ◽  
Raw Materials ◽  
Specific Area ◽  
Extraction Process ◽  
Oil Extraction ◽  
Operating Conditions ◽  
Operating Parameters

Natural precious products such as aroma compounds, essential oils, and bio-activated materials are usually extracted from about 30,000 botanical species. These extracts are often high competitive market due to their small content (less than 1 %) in plants and high purification cost. Thus, development of a modeling for the optimization of the crude oil extraction is highly paid attention. In this work, a modeling of Vietnam lemongrass oil extraction using steam distillation is developed and the optimization of the process parameters is performed using response surface methodology (RSM). The operating parameters considered for the modeling and optimization are specific area of raw materials, moisture content of feedstock, and steam rate. Experimental data show that the oil yield from steam distillation of Vietnam lemongrass is significantly affected by the three mentioned factors. Box-Behnken design (BBD) and analysis of variance (ANOVA) are used to examine the effects of operating parameters on the extraction efficiency. On the basis of the measurements and RSM, a quadric regression model as a function of steam rate, specific area and moisture content of materials is estimated. The optimized operating conditions of the lemongrass hydrodistillation are also obtained by applying the proposed modeling.

Application of Taguchi and response surface methodology approach to a sustainable model developed for a compression-ignition engine using polanga biodiesel/diesel blends

2020 ◽  
pp. 095965182096530
Author(s):  
Abhishek Sharma ◽  
Yashvir Singh ◽  
Avdhesh Tyagi ◽  
Nishant Kumar Singh ◽  
Amneesh Singla
Keyword(s):  
Response Surface Methodology ◽  
Diesel Engine ◽  
Response Surface ◽  
Fossil Fuels ◽  
Fuel Injection ◽  
Injection Pressure ◽  
Biodiesel Fuel ◽  
Injection Timing ◽  
Compression Ignition Engine ◽  
Performance And Emission

The exhaustive and irresponsible use of fossil fuels has created numerous public and environmental health issues in the past few decades. To address this issue, this work has investigated the use of polanga ( Calophyllum inophyllum) biodiesel/diesel blends in a diesel engine. This study focuses primarily on the optimization of performance and emission characteristics of a diesel engine fuelled with polanga-based biodiesel blends. The engine input factors were also investigated for desired optimal thermal performance. In this study, four input parameters, namely, engine loads, blends of polanga-based biodiesel, fuel injection pressure, and fuel injection timing were chosen for analysis. The corresponding engine output responses, namely, brake thermal efficiency, CO, NOx, and smoke emissions, are selected for their optimization by Taguchi method and response surface methodology. The results show that the best setting of above-mentioned input factors is reported at 44% engine load, 13% mixing of polanga biodiesel with diesel, 180 bar injection pressure of fuel, and 21.5 °bTDC injection timing of fuel. The comparison between results obtained by the optimization process and experimental results showed that the deviations were always found to be within the acceptable range of errors.

Prediction-Optimization of the Effects of Di-Tert Butyl Peroxide-Biodiesel Blends on Engine Performance and Emissions Using Multi-Objective Response Surface Methodology (MORSM)

2021 ◽  
pp. 1-26
Author(s):  
Prabhakar Sharma
Keyword(s):  
Response Surface ◽  
Alternative Fuels ◽  
Fossil Fuels ◽  
Objective Response ◽  
Engine Performance ◽  
Percentage Error ◽  
Biodiesel Blends ◽  
Tert Butyl ◽  
Multi Objective ◽  
Butyl Peroxide

Abstract Alternative fuels, such as biodiesel, can be used in place of fossil fuels, although they have a greater viscosity and a longer igniting delay. To compensate for these limitations, several additives are added to biodiesel. The cetane improver Di-Tert Butyl Peroxide (DTBP) was investigated as an additive in this work. DTBP was shown to influence the combustion and emission properties of waste cooking oil biodiesel-diesel blends. The multi-objective response surface technique (MORSM) with Box-Behnken design was used to decrease the number of trials to conserve precious resources such as human effort, time, and money. Theil's uncertainty for the model's predictive capabilities (Theil's U2) was less than 0.1189, demonstrating its robustness. Nash-Sutcliffe efficiency was excellent (0.9885 – 0.9995), with a mean absolute percentage error of less than 1.32%. The engine operating parameters that were optimized were 71.64% engine load, 4964 ppm DTBP additive, and 24.98-degree advance ignition timing. The MORSM-based proposed technique's reliability and robustness validate the usage of DTBP with biodiesel blends, model prediction, and optimization.

scholarly journals Effective Chromium Adsorption From Aqueous Solutions and Tannery Wastewater Using Bimetallic Fe/Cu Nanoparticles: Response Surface Methodology and Artificial Neural Network

2021 ◽  
Vol 14 ◽  
pp. 117862212110281
Author(s):  
Ahmed S. Mahmoud ◽  
Nouran Y. Mohamed ◽  
Mohamed K. Mostafa ◽  
Mohamed S. Mahmoud
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Industrial Effluent ◽  
Oxygen Demand ◽  
Operating Conditions ◽  
Tannery Wastewater ◽  
P Value ◽  
Cu Nanoparticles ◽  
Artificial Neural ◽  
Cr Removal

Tannery industrial effluent is one of the most difficult wastewater types since it contains a huge concentration of organic, oil, and chrome (Cr). This study successfully prepared and applied bimetallic Fe/Cu nanoparticles (Fe/Cu NPs) for chrome removal. In the beginning, the Fe/Cu NPs was equilibrated by pure aqueous chrome solution at different operating conditions (lab scale), then the nanomaterial was applied in semi full scale. The operating conditions indicated that Fe/Cu NPs was able to adsorb 68% and 33% of Cr for initial concentrations of 1 and 9 mg/L, respectively. The removal occurred at pH 3 using 0.6 g/L Fe/Cu dose, stirring rate 200 r/min, contact time 20 min, and constant temperature 20 ± 2ºC. Adsorption isotherm proved that the Khan model is the most appropriate model for Cr removal using Fe/Cu NPs with the minimum error sum of 0.199. According to khan, the maximum uptakes was 20.5 mg/g Cr. Kinetic results proved that Pseudo Second Order mechanism with the least possible error of 0.098 indicated that the adsorption mechanism is chemisorption. Response surface methodology (RSM) equation was developed with a significant p-value = 0 to label the relations between Cr removal and different experimental parameters. Artificial neural networks (ANNs) were performed with a structure of 5-4-1 and the achieved results indicated that the effect of the dose is the most dominated variable for Cr removal. Application of Fe/Cu NPs in real tannery wastewater showed its ability to degrade and disinfect organic and biological contaminants in addition to chrome adsorption. The reduction in chemical oxygen demand (COD), biological oxygen demand (BOD), total suspended solids (TSS), total phosphorus (TP), total nitrogen (TN), Cr, hydrogen sulfide (H2S), and oil reached 61.5%, 49.5%, 44.8%, 100%, 38.9%, 96.3%, 88.7%, and 29.4%, respectively.

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

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