Determination of multi-component flow process parameters based on electrical capacitance tomography data using artificial neural networks

Artificial neural networks have been used to investigate their capabilities at estimating key parameters for the characterisation of flow processes, based on electrical capacitance-sensed tomographic (ECT) data. The estimations of the parameters are done directly, without recourse to tomographic images. The parameters of interest include component height and interface orientation of two-component flows, and component fractions of two-component and three-component flows. Separate multi-layer perceptron networks were trained with patterns consisting of pairs of simulated ECT data and the corresponding component heights, interface orientations and component fractions. The networks were then tested with patterns consisting of unlearned simulated ECT data of various flows and, with real ECT data of gas-water flows. The neural systems provided estimations having mean absolute errors of less than 1% for oil and water heights and fractions; and less than 10° for interface orientations. When tested with real plant ECT data, the mean absolute errors were less than 4% for water height, less than 15° for gas-water interface orientation and less than 3% for water fraction, respectively. The results demonstrate the feasibility of the application of artificial neural networks for flow process parameter estimations based upon tomography data

Development Of A Simulation Toolkit For Electrical Capacitance Tomography.

This paper describes a software tool that implements a two-dimensional finite element (FE) model for the simulation of capacitance readings from a specified electrical capacitance tomography (ECT) system

Oil Height Determination From Capacitance Tomography Measurements Using Neural Network.

This paper presents a "direct" method to gas-oil interface level determination using an artificial neural network approach based on Electrical Capacitance Tomography (ECT) measurements. "Direct" here means that the gas-oil interface levels are obtained directly from the ECT measurements without recourse to image reconstruction. The preliminary work models a separation tank that is filled with gas and oil

Software Toolkit For Designing An Artificial Neural Network.

Basically, there are two kinds of artificial neural network (ANN), which can be classified into supervised and unsupervised. Commonly, supervised neural networks are trained or weights adjusted, so that a particular input leads to a specific target output. Generally, the supervised training methods are commonly used in solving most problems. An ANN can be designed, trained, validated and tested by means of the Neural Network Toolbox in Matlab

Neural Computation For Flow Regime Classification Based On Electrical Capacitance Tomography.

Recognition of gas-liquid flow regimes in pipelines is important in an industrial control process such as for oil production. In oil production, gas-liquid flows are normally concealed in a pipe the actual type of flows cannot be easily determined. Also, obtaining measurements corresponding to the flow distribution becomes almost impossible

Integrating Complementary and Alternative Medicine into Family Medicine Practice: Narrative Review

Complementary and Alternative Medicine (CAM) has witnessed a significant surge in usage across various populations and regions in recent decades. This review article delves into the prevalence and patterns of CAM usage, from cancer patients to cultural practices in Saudi Arabia and New Zealand. In Saudi Arabia, CAM practices, especially spiritual ones like prayer and reciting the Quran, are widely adopted, with herbs, honey, and dietary products also being popular. New Zealand healthcare professionals, including general practitioners and midwives, generally hold a positive view towards CAM, with acupuncture being particularly favored. However, concerns about CAM's scientific evidence, safety, and costs persist. In the U.S., while CAM is popular, many family physicians feel inadequately trained to address CAM-related patient queries. In Germany, a significant number of family physicians use CAM in their practices, emphasizing the need for increased CAM education and research. The data underscores the global trend of CAM adoption and the need for its effective integration into mainstream healthcare. Despite its popularity, the integration of CAM in medical education remains limited in many regions, including Saudi Arabia. However, there's a noticeable shift with some medical schools beginning to incorporate CAM into their curriculum. The article underscores the importance of evidence-based practice, education, training, open communication, regulation, interdisciplinary collaboration, a patient-centered approach, thorough documentation, continuous research, cultural sensitivity, and cost-effectiveness evaluation when considering the integration of CAM into mainstream healthcare. The recommendations provided aim to ensure that patients receive holistic care that is both safe and effective. The overarching theme is the need for a balanced, informed, and collaborative approach to integrating CAM into family medicine practice

Speed Control of a Multi-Motor System Based on Fuzzy Neural Model Reference Method

The direct-current (DC) motor has been widely utilized in many industrial applications, such as a multi-motor system, due to its excellent speed control features regardless of its greater maintenance costs. A synchronous regulator is utilized to verify the response of the speed control. The motor speed can be improved utilizing artificial intelligence techniques, for example fuzzy neural networks (FNNs). These networks can be learned and predicted, and they are useful when dealing with nonlinear systems or when severe turbulence occurs. This work aims to design an FNN based on a model reference controller for separately excited DC motor drive systems, which will be applied in a multi-machine system with two DC motors. The MATLAB/Simulink software package has been used to implement the FNMR and investigate the performance of the multi-DC motor. moreover, the online training based on the backpropagation algorithm has been utilized. The obtained results were good for improving the speed response, synchronizing the motors, and applying load during the work of the motors compared to the traditional PI control method. Finally, the multi-motor system that was controlled by the proposed method has been improved where its speed was not affected by the disturbance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Taif University, TU: TURSP-2020/211Funding: This research was funded by Taif University, project number (TURSP-2020/211), Taif University, Taif, Saudi Arabia