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Accidents on gas pipelines cause significant damage to the national economy and the economy of the state. Thus, it is necessary to always be prepared for such situations in order to restore the normal operation of the gas pipeline as soon as possible. An important role is played by the execution time of the control actions to localize the accident, pump out the gas, and change the operating modes. It is essential that such control be undertaken, especially if such a situation occurs near a gas-measuring installation for measuring the amount of vented gas. Therefore, the control actions must be error-free in order to quickly stop the non-stationary process, which can lead to undesirable consequences. The paper presents a mathematical model of the operation of the pipeline, developed for the management of the pipeline in an emergency. The analysis of the problem of the occurrence of accidents was carried out, and the effect of liquid on its walls was modeled when the operating mode of the pipeline changed. An example is presented using a numerical model carried out in ANSYS, as well as being analyzed analytically. The results of the calculations are presented, and special attention is paid to the parameters influencing the change in the operating mode of the pipeline. [ABSTRACT FROM AUTHOR]

Transportation of fluid is a very important aspect of process industries, especially for oil and gas industries. Pipelines have been considered as the most effective and safest way of transporting fluids. Transportation of gas through a pipeline is an energy-intensive process; hence, energy optimization in gas transportation networks is an important issue to be considered while framing the environmental policies. In this paper, a novel graphical methodology based on pinch analysis approach for simultaneous minimization of capital investment and compression energy requirement in gas allocation network with the aid of thermodynamic relations is developed. The results of the proposed methodology are expressed as a Pareto optimal front. The ε-constraint method is used to generate Pareto optimal front for the two objectives. Identifying the relationship between capital investment and energy requirement gives the opportunity to the decision-maker for choosing the suitable optimal operating point based on the operating and economic conditions of the process. This result allows the planner to calculate the effects via increasing or decreasing energy requirement or capital investment. The applicability of the proposed methodology is demonstrated through two illustrative examples. [ABSTRACT FROM AUTHOR]

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Recently, research focus has shifted from the prevention of hydrate formation in oil/gas pipelines, to the utilization of gas hydrate in various areas of application, such as cold storage, district cooling, and gas transportation. This study, investigated the pressure drop and flow pattern analysis of CO2 hydrate slurry in the presence of anti-agglomerant in a flow loop. A series of experiments were carried out on CO2 hydrate slurry with (10 to 32) % mass fractions at flow rates of (5 to 7) kg/min in the presence of (0, 0.3, 0.5, 0.7 and 1.0) wt.% concentration of Tween 80 (Polyoxyethylene (20) sorbitan monooleate) in a flow loop. The results show that the CO2 hydrate slurry can be divided into two zones based on the pressure drop and temperature behavior; an active formation region, and a less active formation region. It was found that as the hydrate slurry mean flow rate was increased, the gradient of the pressure drop increased. From the flow visualization, three different flow regimes exist for CO2 hydrate slurries, namely homogeneous, heterogeneous, and bedding flow, and those flow regimes were highly dependent on the hydrate fraction in the slurry mixture. [ABSTRACT FROM AUTHOR]

In gas pipeline networks, the set-points should be carefully tuned to minimize the fuel consumption of compressor stations and meet the network requirements. In practice, the real demand has some variations over the forecasted one and consequently utilizing an appropriate controller to minimize the fuel consumption and manage the network variations is inevitable. The model predictive control is a great choice for systems with long delay such as gas networks. In this paper, an intelligent nonlinear model predictive control of a gas pipeline plant is proposed. It models the plant in fully transient state by a multi-layer perceptron neural network. The prediction power of the neural network is used to predict the plant output over a receding horizon. Initially, the network is trained offline and is then paralyzed with the real plant for online training. The proposed strategy consists of two main stages. In the first stage, the compressor set-points are optimized in the open loop condition considering the forecasted demand over a receding horizon and the resulting output pressures are chosen as the reference trajectories for the closed loop system. In the second stage, the controller is applied to compensate the demand variations. The optimization task is carried out using particle swarm optimization gravitation search algorithm (PSOGSA). Numerical results confirm the accuracy and robustness of the proposed controller in the presence of demand variations, noise and uncertainties. [ABSTRACT FROM AUTHOR]

The article considers the technology absorption natural gas dehydration, systematization of the physical processes occurring in the technological chain of preparation of natural gas. Built fuzzy causal model of the process gas drying based on the combined application of fuzzy inference and neural network approximation. The use of the model enables the structural and parametric optimization of production, aimed at improving the quality of preparation of natural gas. [ABSTRACT FROM AUTHOR]