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Showing posts with the label Environmental Science

Evaluation of Emission Pattern of Compression Ignition Engines Fuelled With Blends of Orange Peel Oil Based Biodiesel Using Artificial Neural Network Model

  Abstract It is well established that Orange peeled oil biodiesel (OPOB) is a suitable fuel in Compression Ignition (CI) engines because of its compatible physicochemical properties with diesel. Literature is however sparse on its emission characteristics in CI engines majorly because few investigations have been done due to seemingly difficult and time-consuming experiments. On this strength, this work first carried out experimental investigations on carbon monoxide (CO), unburned hydrocarbon (UHC), oxides of nitrogen (NOX) and smoke of orange peel oil based biodiesel in single cylinder, four stroke CI engine; and afterwards applied the power of artificial neural networks (ANNs) prediction model to predict to the full-scale CO, UHC, NOX, and SMOKE values of the CI engine. Brake load, orange oil-diesel mixture percentages and engine speed were the inputs of the ANN while Levenberg Marquardt (trainlm) and scaled conjugate gradient (trainscg) were the training algor...

Machine Learning Approach to Identify the Relationship Between Heavy Metals and Soil Parameters in Salt Marshes

  Abstract Influences from tidal flooding and freshwater inundation from upland watersheds create an environmentally important ecosystem along coastlines, namely salt marshes. Salt marshes have been recognized as effective sinks for organic carbon and heavy metal contaminants. A detailed understanding of the specific binding agents in the soils on the storage of contaminants is investigated herein using two modern machine learning algorithms: extreme gradient boosting (XGboost) and random forest (RF). Results of the current work indicate that Fe is the most important binding agent for As, Cd, Cr and Zn while Mn and organic matter are the most important binding agents for Cu and Pb. Noting the fact that an increase in salinity not only causes heavy metal release into aquatic systems but also leads to a decrease in floral growth and organic matter production, the findings of this study help to formulate proper remediation strategies to contain heavy metals in tidal ma...