The topside separation system is an important device installed on offshore oil exploration platforms for the treatment of produced water. Due to its operation in high-moisture and salt-infested environments, the system is susceptible to valve malfunctions. Additionally, the presence of strong couplings and slugging disturbances in the system further complicate the development of fault-tolerant control (FTC). To achieve this, this article investigates the fault-tolerant$ H_{\infty } $control problem in the topside separation system. To recover control performance against actuator faults while reducing disturbance sensitivity, the fault-tolerant$ H_{\infty } $control problem is formulated for the topside separation system and is expressed as a two-player differential game problem. A Nash equilibrium solution to the fault-tolerant$ H_{\infty } $control problem is derived by solving the game algebraic Riccati equation (GARE). Considering the tailor-made property and difficulty in full-state sensing in industry, an output feedback reinforcement learning (RL) algorithm is proposed to implement the fault-tolerant$ H_{\infty } $control method without the need for system dynamics. Simulation studies are performed to verify the effectiveness of the proposed algorithm.

In the offshore oil and gas industry, treating produced water, which contains contaminants extracted alongside oil and gas from reservoirs, before it can be safely disposed of or reused is essential. Integrating hydrocyclone systems with three-phase separators significantly improves the efficiency of separating the oil, water, and gas components, thereby ensuring compliance with environmental regulations and reducing operational costs. Consequently, the developed of an optimally coordinated control solution for these integrated systems offers benefits such as enhanced efficiency, cost reduction, regulatory compliance, and improved sustainability. This paper focuses on a comparative study of three control design methodologies: an optimized–PI controller, a model reference adaptive controller (MRAC), and a newly developed Immersion and Invariance (I&I) adaptive controller for offshore produced water treatment. These control strategies are evaluated for their effectiveness in regulating key parameters, such as the pressure drop ratios (PDR) and the water level (L) in the presence of parametric uncertainty and external disturbance. The superior performance of the proposed I&I controller is demonstrated via simulations and analysis.

As the hydrocarbon discharge regulation is becoming more stringent, and emerging sensing and digitization technologies are applied in offshore oil and gas (OG) production, it is beneficial to upgrade the existing control systems by integrating these new measurements/information into the control loops. To achieve this goal, some proper control-oriented models need to be developed. Hydrocyclone-based de-oiling systems are commonly used in the produced water treatment after the three-phase separators in offshore OG production. Control-oriented modeling of these systems has proven a challenging task. In this paper, a system identification approach is taken and polynomial-type Hammerstein-Wiener (HW) models of the separation efficiency are identified for a de-oiling hydrocyclone system using the newly installed online oil-in-water (OiW) measurement. An exhaustive search is used to determine the order of the polynomials and transfer functions. The best model found was a Hammerstein model. The model obtained provides the opportunity to extend the existing de-oiling control framework to include the OiW measurement into feedback loops.

With the objective of pursuing eco-friendly and environmentally sustainable extraction in offshore oil and gas production, the Produced Water (PW) treatment is inescapable whether for reinjecting the PW back to the reservoir with the purpose of enhancing oil recovery or discharging it into the ocean. The de-oiling technology based on the hydrocyclones system connected to the three-phase separator is generally employed for the PW treatment. In order to boost the effectiveness of the interconnected de-oiling systems, it is crucial to introduce an advanced coordinated controller for the separators and hydrocyclone systems with regard to the uncertainties and unknown disruptions that occur during dynamic operations. This research suggests a robust Multivariate Model Reference Adaptive Control (MRAC) approach, in order to regulate the Pressure-Drop-Ratio (PDR) of the hydrocyclone and the water level in the separator simultaneously. The suggested control technique is expressed within a framework of state feedback output tracking MRAC by taking adaptive disturbance rejection into account. Furthermore, a control parametrizing obtained from a factorization of the High-Frequency Gain Matrix (HFGM) in the shape of the product of three matrices, i.e. LDS decomposition, is employed in the designing procedure to relax restrictive conditions about the HFGM. Finally, the proposed robust MRAC method is tested on a lab-scaled PW application, and the simulation outcomes explicitly establish the effectiveness and also its remarkable performance of the suggested control strategy.

By deploying the online Oil-in-Water (OiW) sensors in a de-oiling hydrocyclone system used for produced water treatment processes in offshore oil & gas production, this work investigated modelling of the complicated separation dynamics inside the hydrocylone system using the Long-Short-Term Memory Neural Network (LSTM-NN). The purpose of this modelling is to predict the hydrocyclone's transient de-oiling efficiency in a high level of accuracy. Thereby the hydrocyclone system can be optimally controlled subject to different operating conditions. The acquisition and analysis of the data obtained from a lab-scaled pilot plant is introduced. Two types of LSTM-NN configurations are proposed, and the hyper-parameter tuning as well as training and validation results, are discussed in details. The results exhibit that the relative concentration of OiW, which correlated with the de-oiling efficiency, can be predicted in a quite accurate level using two types of measurements, i.e., the opening degrees of cyclone's underflow and overflow control valves, both the hydrocyclone's inlet/water-outlet OiW concentration measurements. The best model can achieve a normalized RMSE 83,62% accuracy in the validation test. One of our next step is to cooperate the LSTM - NN model into the model predictive control framework to design some optimal control solution for de-oiling hydrocyclone systems.

The output regulation approach has been extensively applied for the cooperative control of heterogeneous multiagent systems (MASs) with known dynamics, but rarely for MASs subject to stochastic and unknown dynamics. One challenging problem is that it is still unclear how to construct the regulator equations of stochastic MASs with unknown dynamics for dynamic exosystem compensation. Toward this end, this article develops a novel distributed control scheme for the approximate output regulation of discrete-time stochastic MASs subject to heterogeneous and unknown nonlinear dynamics. A distributed observer is designed for the exosystem-state estimation of agents, based upon which nonlinear regulator equations are constructed for the feedforward control design, thereby achieving distributed exosystem compensation. A high-order neural network is deployed to approximately solve the nonlinear regulator equations with unknown nonlinearity, and an adaptive control law is developed for the approximate output regulation of discrete-time stochastic MASs. Stability analysis shows that the closed-loop system is semiglobally uniformly ultimately bounded. Simulation results demonstrate the effectiveness and efficiency of the proposed control law.

Three different control solutions, named PID, H∞and MPC, are proposed and compared for a de-oiling water treatment process used in offshore oil and gas production. All control solutions are designed using the plant-wide control strategy, i.e., the considered process consists of the upstream three-phase separator(s) and the downstream de-oiling hydorcyclone(s), such that their dynamic coupling is systematically coped with. The system performances are evaluated according to the conventional control criteria as well as the emerging online Oil-in-Water (OiW) measurement. The experimental results based on a lab-scaled pilot plant illustrate the huge potential and benefit of advanced control solutions to significantly improve the effluent water quality without sacrificing the system’s throughput, particularly when the process is subject to large disturbances (e.g., slugging inflow).

This paper establishes an implementation framework, as a proof of concept, of how to reduce the uncertainty of deploying advanced control offshore. The majority of process research tends to consider improvements in performance, but with less emphasis on how to realize implementation. For control methods to be successfully applied offshore, the methods must be sufficiently simple, trustworthy, and transparent. This is mainly due to the severe consequence of incidences offshore. As it is ultimately the operators that decide which control methods are toggled on/off, the operators need to be aware of the control's behavior. The focus of this paper is not process performance, nor control theory, but rather how to convey the status, state, and action of the controllers to the offshore operators. A design approach is given for displaying and explaining the control for the operators. The is based on uniting the fast prototyping capability of Simulink Real-Time with the graphical capabilities of a Human Machine Interface system. As a case study, experiments are carried out to compare Model Predictive Control to conventional Proportional Integral Derivative control on a scaled offshore pilot-plant, which can emulate different separation processes at the topside of offshore oil & gas installations. The results show that the established connection makes it possible to investigate and compare control systems real-time, which data should be available to an operator and how to represent it.

Maturing Oil & Gas reservoirs in the North Sea result in constant increases of water-cut, which correspondingly poses an increasing strain on the current offshore Produced Water Treatment (PWT) facilities. As one of the key elements of the PWT facilities, the hydrocyclone systems, are responsible for removing the remaining hydrocarbon content in the produced water before the treated water can be discharged, disposed, or reused as injection water. The hydrocyclone's performance heavily correlates with a number of system's and operational parameters, as well as dedicated control strategies. However, these correlations haven't yet been clearly explored from a control oriented point of view, due to the complex separation dynamics inside the cyclone systems. This work investigates and evaluates the hydrocyclone system's operational performance with respect to the most commonly used control paradigm, Pressure Drop Ratio (PDR) control, based on a lab-scaled pilot-plant. It has been clearly observed that in many situations the PDR is not consistently related to the system efficiency or to the inlet flow-rates, however these are assumed consistent in almost all available studies and control designs. In addition, the dynamic responses of PDR and de-oiling efficiency (measured in terms of Oil-in-Water (OiW) concentration) of the deployed hydrocyclone systems are systematically and experimentally studied in this paper. This data can be used to derive control-oriented dynamic models of the considered system, so that a control solution that is better than the current PID-based PDR control, could be developed based on those models.

Sentiment analysis or opinion mining is an important type of text analysis that aims to support decision making by extracting and analyzing opinion oriented text, identifying positive and negative opinions, and measuring how positively or negatively an entity (i.e., people, organization, event, location, product, topic, etc.) is regarded. As more and more users express their political and religious views on Twitter, tweets become valuable sources of people's opinions. Tweets data can be efficiently used to infer people's opinions for marketing or social studies. This paper proposes a Tweets Sentiment Analysis Model (TSAM) that can spot the societal interest and general people's opinions in regard to a social event. In this paper, Australian federal election 2010 event was taken as an example for sentiment analysis experiments. We are primarily interested in the sentiment of the specific political candidates, i.e., two primary minister candidates - Julia Gillard and Tony Abbot. Our experimental results demonstrate the effectiveness of the system.

This paper discusses a way to optimize the speed settings for a multi-pump system such that it can operate with highest efficiency. A short description of an actual multi-pump system is given and the mathematical formulas for single pump and multi-pump systems are presented. Then a set of objectives are formulated which, when used in a MATLAB toolbox implementation of NSGA-II, describe the most efficient distribution of speeds amongst the pumps in the system. The system is tested for a number of pressure references with good results.

This paper presents a variety of different ways of estimating the general parameters for pump-curves. First a formula-tion is made that converts the problem into estimating four parameters in this general formulation. Then three differ-ent methods that utilize the general formulation are pre-sented. The estimated parameters for each method are used in generating the desired pump-curves, and the quality of the estimates are determined. The paper finishes with a recommendation that, for estimation of the generalized pa-rameters, the method utilizing speed differences is preferable over the other methods discussed in the paper. Categories and Subject Descriptors I.6.5 [Computing Methodologies]: Model Development;

This paper investigates the issue of PID-controller parameter tuning for a magnetic levitation system using the non-dominated sorting genetic algorithm (NSGA-II). The magnetic levitation system is inherently unstable and the PID-controller parameters are hard to find using conventional methods. Based on four different performance measures, derived from the step response of the levitation system, the algorithm is used to find a set of non-dominated parameters for a PID-controller that can stabilize the system and minimize the performance measures.

A novel approach for robust restructurable/reconfigurable control synthesis is proposed using a model-matching approach. Based on an augmented system structured by the nominal control system and the faulty system, the objective of restructure/reconfigurable control synthesis is to minimize the difference between the transfer matrix of the reconfigured closed-loop system and that of the nominal closed-loop system in the Hinfin-norm sense, under the constraint that the reconfigured system should be internally stable. Besides regarding the control redesign as a kind of reconfigurable control approach, the control mixer concept is used for synthesis of a restructurable control. The robust control methods, Hinfinand mu synthesis techniques, are employed for these restructurable/reconfigurable control syntheses. The reconfigured system performance, stability and robustness are shown to be guaranteed when some conditions are satisfied. The proposed approach is also extended to deal with parametric and additive faults as well as the case with system uncertainties