Artificial Intelligence (AI) Application in Process Safety Cumulative Risk Visualization for Petroleum Operations: Conceptual Framework (Published)
One of the key challenges in preventing major process safety accidents in an operating plant is the lack of an integrated system/model that brings together the risks posed by the deficiencies / deviations on the safety critical barriers, for operational decision making. Based on this context, a model/framework was developed for assessing and visualizing the accumulation of process safety risks arising from safety critical barriers impairments in petroleum facilities in Niger-Delta Nigeria. Based on the review of the model, the need for an intelligent web-based software was identified. An exploratory study was therefore undertaken through extensive literature review and focused group participants, to develop a conceptual framework for an intelligent web-based software for process safety cumulative risk visualization. The results from the study make it evident that the conceptual framework provides a novel approach in developing an intelligent web-based software using artificial intelligence (AI) techniques, for real time visualization of process safety cumulative risk picture.
Keywords: Artificial Intelligence, cumulative risk assessment, major accident prevention, petroleum operations, process safety
Prediction of Gold associated Mineral worth: An application of mathematically driven artificial neural network technique (Published)
The elemental composition of other associate minerals existing with gold is a significant asset that defines the amount of additional economic contribution that can be obtained from the gold tailings. The elemental composition is a needed factor in increasing the economic value of gold run-off and getting a clear estimation for the quantity of value-added elements in each tonne of gold sand scooped during the separation process. In this study, the artificial neural network (ANN) modeling technique was used to develop an economic worth prediction model for 10 gold-associated minerals. The developed models have a 1:7:10 architecture and were trained using the ANN Bayesian regularization training algorithm. According to the root mean square error values, the results revealed that the predicted values of the associated minerals are closer to the measured values. Also, the developed model prediction performance was found to be appropriate for the estimation of gold-associated mineral economic benefits based on the high coefficient of determination and variance account. The model performance evaluation results show that the developed ANN models are suitable for economic estimation of gold-associated mineral worth.
Keywords: Artificial Intelligence, Gold, Mining, Nigeria, machine learning algorithms, mineral economics