Karim Salahshoor; Babak Roshanipour; Iman Karimi
Abstract
The current paper investigates the influence of packet losses in network control systems (NCS’s) using the model predictive control (MPC) strategy. The study focuses on two main network packet losses due to sensor to controller and controller to actuator along the communication paths. A new Markov-based ...
Read More
The current paper investigates the influence of packet losses in network control systems (NCS’s) using the model predictive control (MPC) strategy. The study focuses on two main network packet losses due to sensor to controller and controller to actuator along the communication paths. A new Markov-based method is employed to recursively estimate the probability of time delay in controller to actuator path and a generalized predictive control (GPC) method is proposed to compensate the effect of big network time-delay, which leads to packet loss. The proposed methods and algorithms have been evaluated using a practical Smar fieldbus pilot plant to judge the efficiency of the foregoing algorithms. The obtained results clearly demonstrate the superiorities of the proposed control scheme with respect to standard MPC algorithm.
Karim Salahshoor; Mohammad Ghesmat; Mohammad Reza Shishesaz
Abstract
This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance ...
Read More
This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter for the final state estimation. The recent data are recursively utilized to apply wavelet transform and extract the variance of the updated data, which makes it suitable to be applied to both static and dynamic systems corrupted by noisy environments. The method has suitable performance in state estimation in comparison with the other alternative algorithms. A three-tank benchmark system has been adopted to comparatively demonstrate the performance merits of the method compared to a known algorithm in terms of efficiently satisfying signal-tonoise (SNR) and minimum square error (MSE) criteria.
Karim Salahshoor; Behnam Lotfi
Abstract
Control of well drilling operations poses a challenging issue to be tackled. The loss of well control could lead to the occurrence of blowout as a severe threat, involving the risk of human lives and environmental and economic consequences. Conventional proportional-integral (PI) controller is a common ...
Read More
Control of well drilling operations poses a challenging issue to be tackled. The loss of well control could lead to the occurrence of blowout as a severe threat, involving the risk of human lives and environmental and economic consequences. Conventional proportional-integral (PI) controller is a common practice in the well control operation. The small existing margin between pore pressure and fracture gradients jeopardizes the efficiency of this conventional method to exercise an accurate and precise pressure control. There is a significant incentive to develop more efficient control methodologies to precisely control the annular pressure profile throughout the well bore to ascertain the down-hole pressure environment limits. Adaptive control presents an attractive candidate approach to achieving these demanding goals through adjusting itself to the changes during well drilling operations. The current paper presents a set of adaptive control paradigms in the form of self-tuning control (STC). The developed STC’s are comparatively evaluated on a simulated well drilling benchmark case study for both regulatory and servo-tracking control objectives. The different sets of test scenarios are conducted to represent the superior performance of the developed STC methods compared to the conventional PI control approach.
Mohammad Khalili; Riyaz Kharrat; Karim Salahshoor; Morteza Haghighat sefat
Abstract
One of the mostly used enhanced oil recovery methods is the injection of water or gas under pressure to maintain or reverse the declining pressure in a reservoir. Several parameters should be optimized in a fluid injection process. The usual optimizing methods evaluate several scenarios to find the best ...
Read More
One of the mostly used enhanced oil recovery methods is the injection of water or gas under pressure to maintain or reverse the declining pressure in a reservoir. Several parameters should be optimized in a fluid injection process. The usual optimizing methods evaluate several scenarios to find the best solution. Since it is required to run the reservoir simulator hundreds of times, the process is very time consuming and cumbersome. In this study a new intelligent method of optimization, called “global dynamic harmony search” is used with some modifications in combination with a commercial reservoir simulator (ECLIPSE®) to determine the optimum solution for fluid injection problem unknowns. Net present value (NPV) is used as objective function to be maximized. First a simple homogeneous reservoir model is used for validating the developed method and then the new optimization method is applied to a real model of one of the Iran oil reservoirs. Three strategies, including gas injection, water injection, and well placement are considered. Comparing the values of NPV and field oil efficiency (FOE) of gas injection and water injection strategies, it is concluded that water injection strategy surpasses its rival. Considering water injection to be the base case, a well placement optimization is also done and best locations for water injection wells are proposed. The results show the satisfying performance of the algorithm regarding its low iterations.
