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Hydraulic calculations are the primary tool for rational technical decisions related to the design and operation of pipeline systems. The compressibility factor is introduced into the gas equation of state to account for its real properties and depends on the pressure, temperature, and gas component composition. At present, the search for an accurate and computationally efficient approximation for the compressibility factor remains an urgent problem. This paper presents a methodology for constructing an approximation based on symbolic regression, and the proposed dependencies analysis provided. The average relative error of the presented models is 0.03%.

In this work we analyze different aspects regarding the terminology, understanding concepts and approaches in modeling and measuring components and variables related to safety and risk. This discussion conversation is open for further interpretations and suggestions. We study it with the help of models (images) artificial, descriptions, scientific approaches, discussions, etc., and using the help of poly-semantic languages. Various kinds of risks arise precisely because of the uncertainty of the situation. Mathematical models use uncertainties in several ways: randomness, which is explained and measured by objective probability and estimated using statistical methods. Uncertainty, measured by subjective probabilities, is estimated by expert methods, or by fuzzy uncertainty methods. Each of these approaches has its advantages and disadvantages. Despite the difference in approaches to measuring uncertainties, the development of a risk situation are described in an appropriate way using an event tree. We show the construction of an event tree, that allows us to see details and specify in the development of a risk situation. We also discuss how this approach can be used to evaluate the sensitivity of the output characteristics of the process to the parameters of the initial information. [ABSTRACT FROM AUTHOR]

The paper is devoted to the modeling of unsteady gas flows in long pipelines. A widely used flow model in the form of a system of partial differential equations obtained by the methods of continuum mechanics is considered. The simplifying assumptions under which it is derived are discussed. It is stated that there is no experimental confirmation of the admissibility of these simplifications. Wave processes observed during the operation of the object are examined. The velocity of pressure waves propagation and attenuation was estimated, some physical phenomena, the causes of which are not clear from the available information, were identified. Actual observations are reconciled with the results of model calculations, some discrepancies between them are detected.

Predictive emission monitoring systems (PEMS) are software solutions for the validation and supplementation of costly continuous emission monitoring systems for natural gas electrical generation turbines. The basis of PEMS is that of predictive models trained on past data to estimate emission components. The gas turbine process dataset from the University of California at Irvine open data repository has initiated a challenge of sorts to investigate the quality of models of various machine learning methods to build a model for predicting CO and NOx emissions depending on ambient variables and the parameters of the technological process. The novelty and features of this paper are: (i) a contribution to the study of the features of the open dataset on CO and NOx emissions for gas turbines, which will enable one to more objectively compare different machine learning methods for further research; (ii) for the first time for the CO and NOx emissions, a model based on symbolic regression and a genetic algorithm is presented—the advantage of this being the transparency of the influence of factors and the interpretability of the model; (iii) a new classification model based on the symbolic regression model and fuzzy inference system is proposed. The coefficients of determination of the developed models are: R 2 = 0.83 for NOx emissions, R 2 = 0.89 for CO emissions. [ABSTRACT FROM AUTHOR]