Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors.

Many different kind of fluids in a wide variety of industries exist, such as two-phase and three-phase. Various combinations of them can be expected and gas-oil-water is one of the most common flows. Measuring the volume fraction of phases without separation is vital in many aspects, one of which is financial issues. Many methods are utilized to ascertain the volumetric proportion of each phase. Sensors based on measuring capacity are so popular because this kind of sensor operates seamlessly and autonomously without necessitating any form of segregation or disruption for measuring in the process. Besides, at the present moment, Artificial intelligence (AI) can be nominated as the most useful tool in several fields, and metering is no exception. Also, three main type of regimes can be found which are annular, stratified, and homogeneous. In this paper, volume fractions in a gas-oil-water three-phase homogeneous regime are measured. To accomplish this objective, an Artificial Neural Network (ANN) and a capacitance-based sensor are utilized. To train the presented network, an optimized sensor was implemented in the COMSOL Multiphysics software and after doing a lot of simulations, 231 different data are produced. Among all obtained results, 70 percent of them (161 data) are awarded to the train data, and the rest of them (70 data) are considered for the test data. This investigation proposes a new intelligent metering system based on the Multilayer Perceptron network (MLP) that can estimate a three-phase water-oil-gas fluid's water volume fraction precisely with a very low error. The obtained Mean Absolute Error (MAE) is equal to 1.66. This dedicates the presented predicting method's considerable accuracy. Moreover, this study was confined to homogeneous regime and cannot measure void fractions of other fluid types and this can be considered for future works. Besides, temperature and pressure changes which highly temper relative permittivity and density of the liquid inside the pipe can be considered for another future idea.

Intelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN.

Two-phase fluids are widely utilized in some industries, such as petrochemical, oil, water, and so on. Each phase, liquid and gas, needs to be measured. The measuring of the void fraction is vital in many industries because there are many two-phase fluids with a wide variety of liquids. A number of methods exist for measuring the void fraction, and the most popular is capacitance-based sensors. Aside from being easy to use, the capacitance-based sensor does not need any separation or interruption to measure the void fraction. In addition, in the contemporary era, thanks to Artificial Neural Networks (ANN), measurement methods have become much more accurate. The same can be said for capacitance-based sensors. In this paper, a new metering system utilizing an 8-electrode sensor and a Multilayer Perceptron network (MLP) is presented to predict an air and water volume fractions in a homogeneous fluid. Some characteristics, such as temperature, pressure, etc., can have an impact on the results obtained from the aforementioned sensor. Thus, considering temperature changes, the proposed network predicts the void fraction independent of pressure variations. All simulations were performed using the COMSOL Multiphysics software for temperature changes from 275 to 370 degrees Kelvin. In addition, a range of 1 to 500 Bars, was considered for the pressure. The proposed network has inputs obtained from the mentioned software, along with the temperature. The only output belongs to the predicted void fraction, which has a low MAE equal to 0.38. Thus, based on the obtained result, it can be said that the proposed network precisely measures the amount of the void fraction.

Dynamic effects of fiscal and monetary policy instruments on environmental pollution in ASEAN.

This study aims to re-examine the impacts of monetary and fiscal policy on environmental quality in ASEAN countries from 1990 to 2019. We utilized the panel and time series NARDL approach to explore the long-run and short-run estimates at a regional level and country level. ASEAN regional-wise analysis shows that contractionary monetary policy reduces the CO2 emissions, while expansionary monetary policy enhances CO2 emissions in the long run. The long-run coefficient further confirms that expansionary fiscal policy mitigates CO2 emissions in ASEAN. The impact of expansionary monetary and fiscal policy on CO2 emissions is positive and significant, while contractionary monetary and fiscal policy have an insignificant impact on CO2 emissions in the short run. ASEAN country-wise analysis also reported the country-specific estimates for the short and long run. Some policies can redesign in light of these novel findings in ASEAN economies.

Controlling environmental pollution: dynamic role of fiscal decentralization in CO2 emission in Asian economies.

The "environment" has become one of the important and debatable topics of the world and policymakers identifying the new predictors of CO2 emissions. Therefore, some economies have been promoting fiscal decentralization to encourage environmental quality by granting more financial autonomy to provincial and sub-national governments. Therefore, this study evaluates the dynamic effect of fiscal decentralization on CO2 in selected nine Asian economies using a fresh dynamic panel ARDL model from 1984 to 2017. The empirical findings show that fiscal decentralization has asymmetric effects on CO2 emissions because a positive change in revenue and expenditure decentralization reduced CO2 emissions in Asia. Moreover, a negative change in expenditure decentralization has also enhanced CO2 emissions in the long run. Thus, clean environmental policies and recommendations can be revised and proposed based on nonlinear findings in the modern era.

Stock markets dynamics and environmental pollution: emerging issues and policy options in Asia.

The fact is the stock market has an asymmetric effect on macroeconomic variables. In this study, we examine the nonlinear stock market reaction to the environment. This is the first study that considers the possibility of asymmetric effects of stock market on environmental pollution in Asia. This study considers the experiences of Asia economies by using the panel NARDL methodology over the data period from 1995 to 2019. The long-run panel NARDL results showed that the positive change in stock market increases carbon emissions. In adverse, the negative change in stock market significantly mitigates the carbon emissions in Asia. The short-run stock market asymmetric effects continued into the long-run asymmetric effects on the environment in Asia. Thus, policymakers and authorities should initiate to promote green financial activities in Asian stock markets.