Biodiesel synthesis from waste cooking oil using heterogeneous acid catalyst: Statistical optimization using linear regression model

2021 ◽  
Vol 13 (4) ◽  
pp. 043101
Author(s):  
Matheus Arrais Gonçalves ◽  
Erica K. Lourenço Mares ◽  
Patrícia Teresa Souza da Luz ◽  
José Roberto Zamian ◽  
Geraldo N. da Rocha Filho ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Acid Catalyst ◽  
Waste Cooking Oil ◽  
Statistical Optimization ◽  
Cooking Oil ◽  
Biodiesel Synthesis ◽  
Heterogeneous Acid Catalyst

scholarly journals Optimization of Biodiesel Production from Waste Cooking Oil Using S–TiO2/SBA-15 Heterogeneous Acid Catalyst

Catalysts ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 67 ◽  
Author(s):  
Muhammad Hossain ◽  
Md Siddik Bhuyan ◽  
Abul Md Ashraful Alam ◽  
Yong Seo
Keyword(s):  
Solid Acid ◽  
Solid Acid Catalyst ◽  
Acid Catalyst ◽  
Waste Cooking Oil ◽  
Biodiesel Production ◽  
Catalyst Loading ◽  
Esterification Reaction ◽  
Impregnation Method ◽  
Cooking Oil ◽  
Heterogeneous Acid Catalyst

The aim of this research was to synthesize, characterize, and apply a heterogeneous acid catalyst to optimum biodiesel production from hydrolyzed waste cooking oil via an esterification reaction, to meet society’s future demands. The solid acid catalyst S–TiO2/SBA-15 was synthesized by a direct wet impregnation method. The prepared catalyst was evaluated using analytical techniques, X-ray diffraction (XRD), Scanning electron microscopy (SEM) and the Brunauer–Emmett–Teller (BET) method. The statistical analysis of variance (ANOVA) was studied to validate the experimental results. The catalytic effect on biodiesel production was examined by varying the parameters as follows: temperatures of 160 to 220 °C, 20–35 min reaction time, methanol-to-oil mole ratio between 5:1 and 20:1, and catalyst loading of 0.5%–1.25%. The maximum biodiesel yield was 94.96 ± 0.12% obtained under the optimum reaction conditions of 200 °C, 30 min, and 1:15 oil to methanol molar ratio with 1.0% catalyst loading. The catalyst was reused successfully three times with 90% efficiency without regeneration. The fuel properties of the produced biodiesel were found to be within the limits set by the specifications of the biodiesel standard. This solid acid catalytic method can replace the conventional homogeneous catalyzed transesterification of waste cooking oil for biodiesel production.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

Synthesis and Characterization of Heterogeneous Solid Acid Catalyst from Alstonia Scolaris Stalks for Biodiesel Production using Waste Cooking Oil

2020 ◽  
Vol 10 ◽  
Author(s):  
Charishma Venkata Sai Anne ◽  
Karthikeyan S. ◽  
Arun C.
Keyword(s):  
Reaction Time ◽  
Solid Acid ◽  
Solid Acid Catalyst ◽  
Acid Catalyst ◽  
Waste Cooking Oil ◽  
Biodiesel Production ◽  
Wood Biomass ◽  
Waste Wood ◽  
X Ray ◽  
Cooking Oil

Background: Waste biomass derived reusable heterogeneous acid based catalysts are more suitable to overcome the problems associated with homogeneous catalysts. The use of agricultural biomass as catalyst for transesterification process is more economical and it reduces the overall production cost of biodiesel. The identification of an appropriate suitable catalyst for effective transesterification will be a landmark in biofuel sector Objective: In the present investigation, waste wood biomass was used to prepare a low cost sulfonated solid acid catalyst for the production of biodiesel using waste cooking oil. Methods: The pretreated wood biomass was first calcined then sulfonated with H2SO4. The catalyst was characterized by various analyses such as, Fourier-transform infrared spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray Spectroscopy (EDS) and X-ray diffraction (XRD). The central composite design (CCD) based response surface methodology (RSM) was applied to study the influence of individual process variables such as temperature, catalyst load, methanol to oil molar ration and reaction time on biodiesel yield. Results: The obtained optimized conditions are as follows: temperature (165 ˚C), catalyst loading (1.625 wt%), methanol to oil molar ratio (15:1) and reaction time (143 min) with a maximum biodiesel yield of 95 %. The Gas chromatographymass spectrometry (GC-MS) analysis of biodiesel produced from waste cooking oil was showed that it has a mixture of both monounsaturated and saturated methyl esters. Conclusion: Thus the waste wood biomass derived heterogeneous catalyst for the transesterification process of waste cooking oil can be applied for sustainable biodiesel production by adding an additional value for the waste materials and also eliminating the disposable problem of waste oils.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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