Dual modification approach for tapioca starch using gamma irradiation and carboxymethylation

Native starches are modified to enhance their characteristics in terms of thermal stability, cold water solubility, and bacterial susceptibility, which limit their industrial applications. In this work, dual modification of tapioca starch by gamma irradiation followed by carboxymethylation was carried out, and the modified starch characteristics were examined. Four dosages of gamma irradiation (25, 35, 45, and 60 kGy) were used for the first modification stage, followed by carboxymethylation using different parameters. The required modification of starch was characterized by FTIR, SEM, TGA, and XRD. Experimental findings showed that the dual modification enhanced the thermal stability of the starch. In addition, carboxymethylation impacted starch's morphology and reduced its crystallinity. Furthermore, the dual-modified starches exhibited excellent characteristics in terms of higher DS values which results in better solublity and could be used in specific applications, including oil and gas, textile, paper, packaging, 3D printing, cosmetics, and pharmaceutical industries

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others

A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion

Funding Information: The authors would like to thank Universiti Teknologi PETRONAS (UTP) Malaysia for giving the opportunity to conduct research under grant number 015LC0-381 for the project “Failure Prediction Model for Stress Corrosion Cracking Using Deep Learning Approach." Publisher Copyright: © 2022 Elsevier LtdTo forecast safety and security measures, it is vital to evaluate the integrity of a pipeline used to carry oil and gas that has been subjected to corrosion. Corrosion is unavoidable, yet neglecting it might have serious personal, economic, and environmental repercussions. To predict the unanticipated behavior of corrosion, most of the research relies on probabilistic models (petri net, markov chain, monte carlo simulation, fault tree, and bowtie), even though such models have significant drawbacks, such as spatial state explosion, dependence on unrealistic assumptions, and static nature. For deteriorating oil and gas pipelines, machine learning-based models such as supervised learning models are preferred. Nevertheless, these models are incapable of simulating corrosion parameter uncertainties and the dynamic nature of the process. In this case, Bayesian network approaches proved to be a preferable choice for evaluating the integrity of oil and gas pipeline models that have been corroded. The literature has no compilations of Bayesian modeling approaches for evaluating the integrity of hydrocarbon pipelines subjected to corrosion. Therefore, the objective of this study is to evaluate the current state of the Bayesian network approach, which includes methodology, influential parameters, and datasets for risk analysis, and to provide industry experts and academics with suggestions for future enhancements using content analysis. Although the study focuses on corroded oil and gas pipelines, the acquired knowledge may be applied to several other sectors.Peer reviewe