Rosdiazli Ibrahim

Bio: Rosdiazli Ibrahim is an academic researcher from Universiti Teknologi Petronas. The author has contributed to research in topics: PID controller & Control theory. The author has an hindex of 16, co-authored 164 publications receiving 1077 citations. Previous affiliations of Rosdiazli Ibrahim include Petronas.

Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealing

Abstract: Development of data-driven virtual flow meter (VFM) using diverse neural network ensembles.Adaptive simulated annealing is used for pruning and combining strategy selection.VFM can provide real-time monitoring for fields with common metering infrastructure.Achieved 4.7% and 2.5% mean absolute errors for gas and liquid flow rate estimations.The proposed method outperforms standard stacking and bagging techniques. Real-time production monitoring in oil and gas industry has become very significant particularly as fields become economically marginal and reservoirs deplete. Virtual flow meters (VFMs) are intelligent systems that infer multiphase flow rates from ancillary measurements and are attractive and cost-effective solutions to meet monitoring demands, reduce operational costs, and improve oil recovery efficiency. Current VFMs are very challenging to develop and very expensive to maintain, most of which were developed for wells with dedicated physical meters where there exists an abundance of well test data. This study proposes a VFM system based on ensemble learning for fields with common metering infrastructure where data generated is very limited. The proposed method generates diverse neural network (NN) learners by manipulating training data, NN architecture and learning trajectory. Adaptive simulated annealing optimization is proposed to select the best subset of learners and the optimal combining strategy. The proposed method was evaluated using actual well test data and managed to achieve a remarkable performance with average errors of 4.7% and 2.4% for liquid and gas flow rates respectively. The accuracy of the developed VFM was also analyzed using cumulative deviation plot where the predictions are within a maximum deviation of 15%. Furthermore, the proposed ensemble method was compared to standard bagging and stacking and remarkable improvements have been observed in both accuracy and ensemble size. The proposed VFM is expected to be easier to develop and maintain than model-driven VFMs since only well test samples are required to tune the model. It is hoped that the developed VFM can augment and backup physical meters, improve data reconciliation, and assist in reservoir management and flow assurance ultimately leading to a more efficient oil recovery and less operating and maintenance costs.

Fractional Order Set-point Weighted PID Controller for pH Neutralization Process Using Accelerated PSO Algorithm

Abstract: Control of pH processes is highly challenging due to high nonlinearity and sensitivity around the equilibrium point. PID controllers being simple and the most widely used perform poorly with changing set-point and are limited to certain operating regions. Thus, they are inadequate for such applications without upgrading. In this paper, a fractional order set-point weighted PID ( $$\mathrm {SWPI^{\lambda }D^{\mu }}$$ ) controller is designed for pH neutralization process. The objective is to achieve adequate set-point tracking and effective load regulation through smooth control action. The controller parameters are tuned using accelerated particle swarm optimization which uses only global best position to achieve faster convergence. Simulation results show that for both standard and industrial configurations of the proposed approach, better performance in terms of set-point tracking, disturbance rejection and sensitivity to model parameter variation is achieved compared to PID, fractional order PID ( $$\mathrm {PI^{\lambda }D^{\mu }}$$ ) and SWPI-D.

Applications of Metaheuristics in Reservoir Computing Techniques: A Review

Abstract: Reservoir computing approaches have been around for almost two decades. They were developed to solve the difficult gradient-descent training of recurrent neural networks. However, in reservoir computing, the choice of parameters and architecture often leads to an optimization challenge. Most early applications have used trial and error with expert knowledge to select the right value for parameter(s)/architecture. This approach is usually cumbersome and difficult considering the large search space. Metaheuristics have been known to perform well in solving such kinds of problems. This review discusses areas where metaheuristics are used in the echo state network—a pioneer in the reservoir computing field. In addition, trends and research gaps are also discussed.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Wireless Networks With RF Energy Harvesting: A Contemporary Survey

Abstract: Radio frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to power the next-generation wireless networks As this emerging technology enables proactive energy replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service requirements In this paper, we present a comprehensive literature review on the research progresses in wireless networks with RF energy harvesting capability, which is referred to as RF energy harvesting networks (RF-EHNs) First, we present an overview of the RF-EHNs including system architecture, RF energy harvesting techniques, and existing applications Then, we present the background in circuit design as well as the state-of-the-art circuitry implementations and review the communication protocols specially designed for RF-EHNs We also explore various key design issues in the development of RF-EHNs according to the network types, ie, single-hop networks, multiantenna networks, relay networks, and cognitive radio networks Finally, we envision some open research directions

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Micropower energy harvesting

Abstract: More than a decade of research in the field of thermal, motion, vibration and electromagnetic radiation energy harvesting has yielded increasing power output and smaller embodiments. Power management circuits for rectification and DC-DC conversion are becoming able to efficiently convert the power from these energy harvesters. This paper summarizes recent energy harvesting results and their power management circuits.