The occurrence and distribution of microplastics (MPs) for two seasons (dry and raining) were investigated based on 10 sections of OX-Bow Lake Yenagoa, Nigeria for surface water and sediments. MPs were abundant in colour and dominated by fibrous items. For dry season, Polyethylene terephthalate (PET) and Plasticised polyvinyl chloride (Plasticised PVC) were the predominant MPs; they both account for 72.63% and 10.9% of surface water and sediment samples. The raining season accounted for Plasticised (PVC) 81.5% and low-density polyethylene 4.2% respectively. The raining and dry seasons MPs were characterise by μ-FTIR. Beads and pellets were most common MP shapes in both water and sediment samples for the two seasons. The results showed that there is high presence of MPs in OX-Bow Lake.

Drilling fluids are the most important materials in drilling operations, therefore improving the properties of these fluids are very essential in order to meet up with the increase in demands and required standards. In this experimental study, Solanum tuberosum formulated biopolymer was used to improve the water based mud rheological properties and artificial neural network predicted data for (PV) plastic viscosity, (AP) apparent viscosity and (YP) yield point. Artificial neural network (ANN) was used to train the rheological properties of the formulated mud and the network developed predicted the rheological properties of an untrained combination of bentonite and modified biopolymer. The main target is to regenerate or predict the rheological properties of the formulated mud; (AP) apparent viscosity, (YP) yield point and (PV) plastic viscosity generated originally from experimental procedures but this time using the ANN. The mean average error target was set to around 5-10%. As a model for choosing the best ANN architecture for predicting target value, two statistical parameters, mean squared error (MSE) and correlation coefficient (R2) were utilized. A system with the lower estimations of MSE and the higher estimations of R2 gives more precise forecasts. Three different network were created and the two statistical parameters were used to determine the best number of neurons in the hidden layer. The developed neural network with best prediction has Root Mean Square Error (MSE) of 1.25221 and overall correlation coefficient (R2) of 0.99373 for the predicted plastic viscosity, yield point and apparent viscosity

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Background: A binary mixture of sesame and castor oil was used for reducing the corrosion rate of mild steel in crude oil environments. This study investigated the corrosion behavior of a binary mixture of sesame and castor oil as a corrosion inhibitor for mild steel in crude oil. Different parameters such as immersion time, the concentration of inhibitor and pH were investigated for corrosion of mild steel. Methods: Experimental analysis indicates that a passive layer of the inhibitor formed over the surface of mild steel thereby reducing the corrosion rate. This was demonstrated by varying different process parameters such as the concentration of binary inhibitor, pH and time using two different statistical models; the Response Surface Methodology (RSM) and the Artificial Neural Network (ANN). Results: From the results, it was observed that ANN was a better predictive tool to determine the corrosion rate of mild steel than the RSM. Conclusion: Overall, both the models prove that relative to the process parameters used, the importance level of the parameters was Time < Concentration of binary inhibitor < pH.

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