The transportation of wet gas fluid in carbon steel pipelines for onshore processing offers an economically attractive strategy. Although a substantial saving in capital cost can be realised, the risks of hydrate formation and corrosion damage are two of the main issues with such an approach. The standard industrial practice is to apply chemical solutions to reduce the risks. A thermodynamic hydrate inhibitor, such as monoethylene glycol (MEG) and corrosion inhibitors are commonly utilized to provide hydrate and corrosion control, respectively. Other production chemicals, such as an oxygen scavenger, may also be deployed as part of the risk management process. Consequently, the main challenge to the corrosion inhibitor is to provide corrosion protection throughout the production and processing facility while subjected to high temperatures in the MEG regeneration process and exposure to other production chemicals. Thermal stability and performance assessments should be an important aspect of the qualification process in the selection of corrosion inhibitors. This paper presents data from laboratory corrosion inhibitor evaluation programs, using thermally stressed MEG/chemicals under simulated wet gas pipeline operating conditions, which resulted in the successful qualification of a corrosion inhibitor for the production facility. In addition, the performance of oxygen scavengers for use in MEG systems is reviewed, including details of an oxygen scavenger that performs in lean MEG.

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The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al2O3-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model.

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