---

Surge and rotating stall phenomena are two dynamic instabilities that occur in both axial and centrifugal compressors. Surge is the stream instability phenomenon in compressor that imposes severe damages to the compressors. Nowadays, suppressing surge phenomenon is one of the most important issues in oil and gas industries, especially when flow reduction or gas reflux is considered. This research seeks to extract the required technical information about control lines, surge lines, and to present a new combined method to determine the performance curve of 6 rows of gas compressors in Asmari Kupal gas pressure boost station (National Iranian South Oil Company) made in Germany by MAN BORSIG Company, and to design a smart controller in order to increase the reliability of the control system and improve the machine performance. Finally, the system performance validity is shown by simulating a surge characteristic curve and implementing two points of the compressor operation condition  

---

Surge is one of the two destructive factors in compressors. surge is the stream instability phenomenon in compressor that imposes severe damages to the compressors. Nowadays, suppressingsurge phenomenon is one of the most important issues in oil and gas industries, especially when flow reduction or gas reflux is considered. According to Moore-Greitzer compressor model, this paper designs an active controller for surge control in constant speed centrifugal compressors. As such, the applied operator considered for surge control is Close Couple Valve (CCV) and it is designed to stabilize a centrifugal compressor system with disturbaces using nonlinear predictive controller. The proposed controller, without any information about the amount of Throttle Valve variations, could control the surge instability and reduce the distance between compressor operation point and the surge line. Finally, the compressor system with controller is simulated and the obtained results will show the efficiency of the designed nonlinear predictive controller

---

In this paper, a novel methodology is proposed to improve performance of the Networked Control System (NCS) in the face of random time-delays, using Model Predictive Controller (MPC) approach. For this purpose, a new state-feedback MPC structure is developed to cope with random network time-delays when the system is subjected to uncertainties with state and control constraints. The main idea is to reduce the disturbing effect of random network time-delays on regulatory performance of the NCS using a new robust formulation in MPC design. A terminal penalty constraint has been added to the finite horizon objective function to guarantee the stability of the system stability. Finally, applicability of the presented method is evaluated in a real pilot plant within a NCS configuration, being realized by an industrial Ethernet and Foundation Fieldbus technology. It is demonstrated that the proposed online methodology is effective to provide a better performance, having faster response, smaller overshoot and stronger robustness compared to the conventional MPC method with less aggressive control actions.  

---

Oil pipeline leakages, if not properly treated, can result in huge losses. The first step in tackling these leakages is to diagnose their location. This paper employs a data-driven Fault Detection and Isolation (FDI) system not only to detect the occurrence and location of a leakage fault, but also to estimate its severity (size) with extreme accuracy. In the present study, the Golkhari-Binak pipeline, located in southern Iran, is modeled in the OLGA software. The data used to train the data-driven FDI system is acquired by this model. Different leakage scenarios are applied to the pipeline model; then, the corresponding inlet pressure and outlet flow rates are recorded as the training data. The time-domain data are transformed into the wavelet domain; then, the statistical features of the data are extracted from both the wavelet and the time domains. Each of these features are then fed into a Multi-Layer Perceptron Neural Network (MLPNN) which functions as the FDI system. The results show that the system with the wavelet-based statistical features outperforms that of the time-domain based features. The proposed FDI system is also able to diagnose the leakage location and severity with a low False Alarm Rate (FAR) and a high Correct Classification Rate (CCR).

---

Drilling is one of the most critical mechanical process in oil and gas industry in which its operational parameters should properly be tuned to reduce drilling time and consequently enhance efficiency of the drilling process. The main objective in this paper is to present a new method to regulate and optimize the Rate of Penetration (ROP) of the system with top drive rotary motor torque in drill string. The paper presents a formulation of a robust receding horizon controller to track piecewise constant references. To achieve this, a tube-based Robust Model Predictive Control (RMPC) is introduced in which the tubes are based on reachable sets. A drilling system is assumed as a test bed for evaluating the performance of the proposed control scheme. The assumed drilling system is modeled as a linear system with additive bounded uncertainties by using Bourgoyne and Young model which is known as a complete mathematical drilling model. The most important novelty part of this manuscript corresponds to integration of both tracking and regulatory objectives in one control framework. Simulations demonstrate the effectiveness of the stability and robust characteristics the proposed RMPC scheme in terms of its stability and robust characteristics with respect to the usual control approaches.

---

To transport the natural gas from the producer sources to the consumers, gas pipelines are used. The extension of the pipelines reduces gas pressure and compression stations are considered to compensate for the loss of pressure in the transport path. At these stations, several compressor units usually work in parallel to share the gas inlet flow. The purpose of the present study is to investigate the load distribution method between parallel compressors in an optimal manner according to the capacity of each compressor. In this paper, a load sharing algorithm is proposed with the help of a model-based predictive controller (MPC) to achieve a stable and efficient operation at the compression station. In this algorithm, real-time optimization is used by an adaptive modifier method when faced with a variable demand from the consumer. The optimization is done according to the capacity of each compressor and using the approximated efficiency curve. In this way, the coordinates of the optimal working points for each compressor are obtained. The proposed algorithm is implemented in MATLAB software on the dynamics of centrifugal compressors in parallel arrangement. The results show that the load distribution using the proposed method has a much better performance than the old methods such as equal load balancing. This method results in the optimal operation of each compressor and thus storing and saving the natural gas.