When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent.

Setting up pipelines in the oil industry is very costly and time consuming. For this reason, a pipe is usually used to transport various petroleum products, so it is very important to use an accurate and reliable control system to determine the type and amount of oil product. In this research, using a system based on the gamma-ray attenuation technique and the feature extraction technique in the frequency domain combined with a Multilayer Perceptron (MLP) neural network, an attempt has been made to determine the type and amount of four petroleum products. The implemented system consists of a dual-energy gamma source, a test pipe to simulate petroleum products, and a sodium iodide detector. The signals received from the detector were transmitted to the frequency domain, and the amplitudes of the first to fourth dominant frequency were extracted from them. These characteristics were given to an MLP neural network as input. The designed neural network has four outputs, which is the percentage of the volume ratio of each product. The proposed system has the ability to predict the volume ratio of products with a maximum root mean square error (RMSE) of 0.69, which is a strong reason for the use of this system in the oil industry.

A supplemental pozzolanic material such as fly ash may result in a reduction in the concrete’s adverse environmental effect by reducing the discharge of carbon dioxide throughout the cement production procedure. This pozzolanic material also enhances the mechanical characteristics as well as the durability of concrete material. Considering the boundless passion for utilizing fly ash and conducting extensive research studies, the extent to which this supplement can be added to concrete has a limitation equal to almost one-third of cement material’s weight. In the current study, a model based on the Radial Basis Function (RBF) is developed to estimate the compressive strength of concrete containing various amounts of fly ash at any arbitrary age. Having parameters used as inputs in ANN modeling such as concrete additives and characteristics of fly ash, the output was compressive strength. It was concluded that the estimated results agree well with the experimental measurements with an MSE of 0.0012 for the compressive strength. Simple and practical equations are proposed to present a simple means to determine the compressive strength of fly ash-based concrete.

As the oil and petrochemical products pass through the oil pipeline, the sediment scale settles, which can cause many problems in the oil fields. Timely detection of the scale inside the pipes and taking action to solve it prevents problems such as a decrease in the efficiency of oil equipment, the wastage of energy, and the increase in repair costs. In this research, an accurate detection system of the scale thickness has been introduced, which its performance is based on the attenuation of gamma rays. The detection system consists of a dual-energy gamma source (²⁴¹ Am and ¹³³ Ba radioisotopes) and a sodium iodide detector. This detection system is placed on both sides of a test pipe, which is used to simulate a three-phase flow in the stratified regime. The three-phase flow includes water, gas, and oil, which have been investigated in different volume percentages. An asymmetrical scale inside the pipe, made of barium sulfate, is simulated in different thicknesses. After irradiating the gamma-ray to the test pipe and receiving the intensity of the photons by the detector, time characteristics with the names of sample SSR, sample mean, sample skewness, and sample kurtosis were extracted from the received signal, and they were introduced as the inputs of a GMDH neural network. The neural network was able to predict the scale thickness value with an RMSE of less than 0.2, which is a very low error compared to previous research. In addition, the feature extraction technique made it possible to predict the scale value with high accuracy using only one detector.

As time passes, scale builds up inside the pipelines that deliver the oil or gas product from the source to processing plants or storage tanks, reducing the inside diameter and ultimately wasting energy and reducing efficiency. A non-invasive system based on gamma-ray attenuation is one of the most accurate diagnostic methods to detect volumetric percentages in different conditions. A system including two NaI detectors and dual-energy gamma sources (²⁴¹Am and ¹³³Ba radioisotopes) is the recommended requirement for modeling a volume-percentage detection system using Monte Carlo N particle (MCNP) simulations. Oil, water, and gas form a three-phase flow in a stratified-flow regime in different volume percentages, which flows inside a scaled pipe with different thicknesses. Gamma rays are emitted from one side, and photons are absorbed from the other side of the pipe by two scintillator detectors, and finally, three features with the names of the count under Photopeaks ²⁴¹Am and ¹³³Ba of the first detector and the total count of the second detector were obtained. By designing two MLP neural networks with said inputs, the volumetric percentages can be predicted with an RMSE of less than 1.48 independent of scale thickness. This low error value guarantees the effectiveness of the intended method and the usefulness of using this approach in the petroleum and petrochemical industries.

Given that one of the most critical operations in the oil and gas industry is to instantly determine the volume and type of product passing through the pipelines, in this research, a detection system for monitoring oil pipelines is proposed. The proposed system works in such a way that the radiation from the dual-energy source which symmetrically emits radiation, was received by the NaI detector after passing through the shield window and test pipeline. In the test pipe, four petroleum products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated in pairs in different volume fractions. A total of 118 simulations were performed, and their signals were categorized. Then, feature extraction operations were started to reduce the volume of data, increase accuracy, increase the learning speed of the neural network, and better interpret the data. Wavelet features were extracted from the recorded signal and used as GMDH neural network input. The signals of each test were divided into details and approximation sections and characteristics with the names STD of A3, D3, D2 and were extracted. This described structure is modelled in the Monte Carlo N Particle code (MCNP). In fact, precise estimation of oil product types and volume fractions were done using a combination of symmetrical source and asymmetrical neural network. Four GMDH neural networks were trained to estimate the volumetric ratio of each product, and the maximum RMSE was 0.63. In addition to this high accuracy, the low implementation and computational cost compared to previous detection methods are among the advantages of present investigation, which increases its application in the oil industry.