Electrical and Electronic Engineering

The need for fault detection and diagnosis (FDD) is becoming increasingly important for industrial processes as the complexity of processes increases. A state space model of a counterflow heat exchanger is used as a case study to illustrate the usefulness of energy-based FDD. Energy is regarded as a unifying parameter to uniquely describe the state of the heat exchanger under fault conditions of fouling and heat leakage. Both steady state and transient energy-based residuals are proposed for the purpose of FDD. The results indicate that the use of energy-based residuals seems viable and warrant further investigation.

In this paper, optimal linear control techniques are utilised to control a radio-controlled helicopter (30 size) in the AeroSIMRC simulation environment. A grey-box time-domain system identification method is used to estimate a linear state space model that operates in hover mode. Identifying the unknown parameters in the model is highly dependent on the initial values and the input data. The model is divided into subsystems to make estimation possible. The identified state space model shows a good measure of fit compared to the simulator's flight data. A linear quadratic controller forms the inner-loop, and an optimised PID outer-loop generates attitude commands from a given inertial position trajectory. An observer estimates the unmeasured states such as blade flapping. The controller is developed in Simulink ® with a plug-in written for the AeroSIMRC flight simulator. The plug-in enables Simulink ® to control the simulator through a User Datagram Protocol interface for the purpose of model and controller validation. The proposed methodology facilitates a low cost test environment for new flight control algorithms. The simulation results show that the controller keeps the helicopter stable in the presence of considerable environmental disturbances and plant uncertainties.

This paper is concerned with the control of an under-actuated, uncertain, delayed non-linear system through the implementation of artificial neural networks(ANNs). The aim is the development of a series-parallel training scheme for the on-line observer to ensure faster convergence and more accurate estimations. Reinforcement learning is used to improve future performance and maintain stability while an estimated tracking error is minimised. Lyapunov stability measures are employed to guarantee the uniform ultimate boundedness of the closedloop tracking error. Real-time learning algorithms are derived for the individual components (observer, actor, critic). Final performance is tested on a mathematical helicopter model and real-world helicopter flight data.

Energy is a universal concept that can be used across physical domains to describe complex large-scale industrial systems. This brief survey and framework gives a perspective on energy as a unifying domain for system modelling, supervision, and control. Traditionally, modelling and control problems have been approached by adopting a signal-processing paradigm. However, this approach becomes problematic when considering non-linear systems. A behavioural viewpoint, which incorporates energy as basis for modelling and control, is considered a viable solution. Since energy is seen as a unifying concept, its relationship to Euler-Lagrange equations, state space representation, and Lyapunov functions is discussed. The connection between control and process supervision using passivity theory coupled with a system energy balance is also established. To show that complex industrial systems comprising multiple energy domains can be modelled by means of a single electric circuit, its application to a large-scale thermo-hydraulic system is presented. Next, a simple non-linear transmission impedance electric circuit is used to illustrate how energy can be used to not only describe a system, but also serve as basis for system optimisation. An energy-based framework is proposed whereby energy is used as a unifying domain to work in, to analyse, and to optimise large-scale industrial systems.

The hydrocyclone is widely used throughout the mineral processing industry when working with slurries. It is either used for classifying, desliming or dewatering. Hydrocyclones are inexpensive, application-efficient and relatively small to employ. In order to quantify its separation efficiency, models are utilised to estimate the cut-size and sharpness of classification coefficient, usually in the form of a partition curve. Most models are based on experimentally obtained data and are therefore not always universally applicable. Over the last decade researchers have started employing Artificial Neural Networks (ANNs) in order to obtain a dynamic model. This study endeavoured to use experimentally acquired data to develop models that predict the cut-size. The models are discussed and evaluated in detail and the best predicting model was compared to a conventional model from literature.

This paper presents a sizing and control optimisation architecture for the design and evaluation of a small-scale stand-alone hybrid PV-wind-battery system for the production of hydrogen (H 2) using proton exchange membrane (PEM) technology. Three objectives are considered simultaneously namely cost, efficiency and reliability. For this task an optimisation approach is developed combining a single objective genetic algorithm (GA) with a multiobjective GA (MOGA) to optimise nine system sizing variables and six power management system control set-point variables. The nine sizing and six control variables are combined to form a solution vector. The optimisation algorithm searches the search space, with user defined boundaries, for non-dominated solution vectors. The result is a set of solution vectors which are useful in the selection of components for the design and evaluation of these systems. The optimisation approach developed sufficiently searches the bounded search space and provides results in the form of a set of non-dominated solution vectors. These results are useful in understanding how the different components of such a non-linear complex system affect each other as well as the three objectives considered in this study.

Flywheel Energy Storage (FES) is rapidly becoming an attractive enabling technology in power systems requiring energy storage. This is mainly due to the rapid advances made in Active Magnetic Bearing (AMB) technology. The use of AMBs in FES systems results in a drastic increase in their efficiency. Another key component of a flywheel system is the control strategy. In the past, decentralised control strategies implementing PID control, proved very effective and robust. In this paper, the performance of an advanced centralised control strategy namely, Model Predictive Control (MPC) is investigated. It is an optimal Multiple-Input and Multiple-Output (MIMO) control strategy that utilises a system model and an optimisation algorithm to determine the optimal control law. A first principle state space model is derived for the purpose of the MPC control strategy. The designed MPC controller is evaluated both in simulation and experimentally at a low operating speed as a proof of concept. The experimental and simulated results are compared by means of a sensitivity analysis. The controller showed good performance, however further improvements need to be made in order to sustain good performance and stability at higher speeds. In this paper advantages of incorporating a system model in a model-based strategy such as MPC are illustrated. MPC also allows for incorporating system and control constraints into the control methodology allowing for better efficiency and reliability capabilities.

Fault detection and isolation (FDI) within the petrochemical industries (PCIs) is largely dominated by statistical techniques. Although a signal-based technique centered on exergy flows within a process plant was proposed, it has only been applied to single process units. The exergy-based scheme has not yet been applied to process plants that feature at least a single recycle stream. The Tennessee Eastman process (TEP) is commonly used as an FDI benchmark process, but due to obfuscation, the TEP cannot be directly implemented in a commercial process simulator. Thus, application of FDI techniques to proprietary processes will require significant investment into the implementation of the FDI scheme. This is a key impediment to the wide-spread comparison of various FDI techniques to non-benchmark processes. In this paper, a gas-to-liquids (GTL) process model is developed in Aspen HYSYS®, and the model’s performance is validated. The exergy-based FDI technique is applied to the GTL proc...

As the complexity of industrial systems increases, the necessity also increases for more reliable, robust and accurate Fault Detection and Diagnosis (FDD) techniques. This article proposes a methodology for the design of an FDD system, based on an energy visualisation of an axial-flow compressor system. This method investigates the steady state and transients of the system for residual generation. A nonlinear model is derived for this compressor system that are capable of modelling compressor instabilities such as rotating stall, surge and leaks. A power-energy analysis is done using the state variables of the system under normal and fault conditions and are visually presented. Two fault conditions are analysed in terms of power and energy. The first test case introduces a leak in the system. The second focuses on the more complex situation when the system enters an unstable operation, namely rotating stall. The results indicate that the power and energy of the system may be useful for the purposes of FDD.

The focus of this paper is the development of attributed graph representations of industrial processes. In this case energy attributes are used since it serves a data reduction purpose and allows for the consideration of multi-domain systems. Pattern recognition approaches towards FDI are considered advantageous due to their visual interpretation qualities. It is therefore envisaged that these attributed graphs can be used in a new innovative graph matching methodology to be able to detect and isolate faults. A two-tank thermo-fluid system is considered in this paper as a case study. An attributed graph containing exergy and energy flows is derived and from this graph node signature matrices are extracted that represent normal and fault conditions. Fault signatures are compared to the normal signature by deriving a cost matrix using a Heterogenous Eucledian-Overlap Metric (HEOM). Eigenvalues of the cost matrices are analysed in a qualitative way as a first stage of fault detection.

Fluidised bed granulation is a widely used wet granulation technique. The operation of a fluidised bed granulator (FBG) as well as the quality of the product is strongly influenced by multiple process variables and disturbances. Controlling this process is difficult due to long lag times between sample analysis. Inference sensors are therefore an effective control solution for this complex process. A continuous industrial FBG was used to develop multiple linear regression (MLR) models that included two-way interaction effects. Elementary artificial neural network (ANN) models were developed to qualitatively assess the MLR models. The influences of the fluidizing air, the spray liquid and the seed particle size on the product quality were investigated and modelled. The spray liquid was found to have the largest correlation with the quality variables. Both modelling techniques produced accurate models, however undertraining of some ANN models resulted in a larger deviation between the model and validation data.

The exergy-based fault detection method has not yet been applied to a complex industrial system that adequately represents a dynamically changing process. One such system, the Tennessee Eastman process, is commonly used as a benchmark for fault detection methods. In this paper, an exergy-based fault detection approach is applied to the Tennessee Eastman process. This is done to investigate the feasibility of using this approach when confronted with noisy sensor data and control loops masking faulty behaviour. An exergy characterisation was performed on stream data obtained from the Tennessee Eastman process. The exergy characterisation included a new approach to calculate the standard chemical exergy of unknown components. For fault detection, threshold limits were determined for the exergy characterisation when normal operating conditions are assumed. The threshold limits were calculated following the upper and lower control limit determination of the Shewhart control chart. The results showed that this method could quantify both the physical state as well as the chemical features of the process and that 17 out of the 20 considered faults could be detected. This shows that the exergy-based method could be adequately applied to the Tennessee Eastman process.

The unique working mechanics of roller-type peristaltic pumps have allowed their applications to span a wide variety of sectors and industries. The roller-type pump's accurate dosing and hydrostatic capabilities can theoretically allow for the pump to be used for hydraulic actuation (as an electro-hydrostatic actuator) for low pressure applications. This however requires accurate control of the peristaltic pump and its flow rate. The associated pressure pulsations will also have an impact on the pump's selection criteria. Accurate control of roller-type peristaltic pumps commonly rely on flow-meters, which increases the cost of the pump and can complicate control strategies. Current modelling approaches either do not rely on first principle modelling and require expensive simulation software, or do not apply for larger Reynolds numbers at larger flow rates. The most applicable model focusses on the flow rate for each roller individually. This implies that the model requires ...

The need exists for a benchmark multi-domain practical system that could emulate typical process faults for the purposes of condition monitoring. A proposition was made to develop a heated two-tank system, due to its multi-domain nature i.e. thermal-fluid, mechanical, electrical as well as its non-linear nature. The paper presents an analysis of a helical coil heat exchanger. A comparison is made between steam and water as a heat transfer medium. The study is conducted to determine which medium will be the most feasible to implement in a laboratory scale two-tank heated system. The convection coefficients on the hot and cold side of the helical coil are calculated to determine the inlet and outlet temperatures of the system. The temperatures are compared at various mass flows.

Each year, latest state of the art technologies and algorithms arise that claim and prove to-shine their predecessors. One such algorithm is the Colour-based Retina Key-point (CREAK) descriptor, which is based on the FAST Retina Key-point (FREAK) descriptor with the included functionality of considering colour information in its Key-point description. This paper explores the implementation of CREAK in a “real-time” visual odometry application by means of a comparative study of the more well-known FREAK algorithm. Although FREAK achieved more accurate odometry when key-points were abundant, this proved to be too computationally expensive. CREAK on the other hand outperformed FREAK when key-points are scarce due to its lack of false-positive matches.

Electrochemical impedance spectroscopy (EIS), current interrupt (CI) and current mapping (CM) were investigated as in-situ characterisation tools for PEM electrolysers. A 25 cm 2 cell with titanium anode and carbon cathode plates were utilised in this study. A commercial MEA consisting of 1 mg IrO 2 /cm 2 on the anode and 0.3 mg Pt/cm 2 on the cathode was used. The electrocatalyst was deposited on Nafion Ò membranes. The electrochemical losses in a PEM electrolyser namely: activation, ohmic and mass transfer losses were identified using EIS and CI and both the advantages and disadvantages of the methods were discussed. The current distribution over the membrane electrode assembly (MEA) at different current densities was measured using the current mapping method. It is also shown that under the given experimental conditions the current density decreases along the serpentine flow field.

The need to characterise a PEM electrolyser is motivated by a South African hydrogen company. One of two electrochemical characterisation methods, namely the current interrupt method or electrochemical impedance spectroscopy, is investigated to characterise the PEM electrolyser. Various literature sources can be found on the electrochemical characterisation methods. In this study the current interrupt method is used for the electrochemical characterisation of a PEM electrolyser. The current interrupt method is an electrical test method that will be used to obtain an equivalent electric circuit model of the PEM electrolyser. The equivalent electric circuit model relates to various electrochemical characteristics such as the activation losses, the ohmic losses and the concentration losses. Two variants of the current interrupt method, namely the natural voltage response method and the current switching method, are presented. These methods are used to obtain two different equivalent electric circuit models of the PEM electrolyser. The parameters of the first equivalent electric circuit, namely the Randles cell, will be estimated with the natural voltage response method. The parameters of the second equivalent electric circuit, namely the Randles-Warburg cell, will be estimated with the current switching method. Simulation models of the equivalent electric circuits are developed and tested. The simulation models are used to verify and validate the natural voltage response method and the current switching method. The parameters of the Randles cell simulation model is accurately calculated with the natural voltage response method. The parameters of the Randles-Warburg cell simulation model is accurately calculated with the current switching method. The natural voltage response method and the current switching method are also practically implemented. The results is used to indicate the various electrochemical characteristics of the PEM electrolyser. A Nafion 117 type membrane was tested with iv the current interrupt method. The membrane resistance parameters of Randles cell were estimated with the natural voltage response method. These values are validated with conductivity measurements found in literature. The results of the Randles-Warburg cell is validated with a system identification validation model.

Control engineers have been aware of non-minimum phase systems showing either undershoot or time-delay characteristics for some considerable time (Linoya & Altpeter, 1962; Mita & Yoshida, 1981; Vidyasagar, 1986; Waller & Nygardas, 1975). A number of researchers that addressed this problem from a predictive control point of view mainly followed one of two approaches: a classical (non-optimal) predictive approach or a modern optimisation based predictive approach (Johnson & Moradi, 2005). The common characteristic of all these approaches is that they are model-based. Predictive control allows the controller to predict future changes in the output signal and to use this prediction to generate a desirable control variable. The classical predictive controllers that are most widely considered include the Smith predictor structure and the internal model control (IMC) structure (Katebi & Moradi, 2001; Morari & Zafiriou, 1989; Tan et al., 2001). Modern predictive controllers consider generalised predictive control (GPC) or model-based predictive control (MPC) structures

This paper considers the development of a PID control strategy to optimally control the power output of a High Temperature Gas-cooled Reactor (HTGR) power plant. A specific type of HTGR called the Pebble Bed Modular Reactor (PBMR) that utilises a closed recuperative Brayton cycle with helium as working fluid is considered. The power control of this kind of plant is significantly different from conventional steam cycle nuclear power plants. A distinguishing feature that complicates the control is the use of three separate shafts for different compressor/turbine or turbine/generator pairs. In addition the power output cannot be directly controlled by means of an upstream valve that regulates the flow through the power turbine, as is the case with conventional steam cycles. This paper addresses these challenges by means of a control strategy consisting of four PID control loops. The controller gains are optimised by means of a Genetic Algorithm (GA) that uses real-valued genes and the ITAE performance measure as a cost function. The control strategy is implemented and evaluated on a linear Simulink ® model of the PBMR Power Conversion Unit (PCU). Results are presented illustrating the performance of the GA optimised PID control strategy.

The Reactor Cavity Cooling System (RCCS) in a High Temperature Gas-cooled Reactor (HTGR) provides protection to the concrete structures surrounding the reactor pressure vessel. The RCCS comprises stand pipes circulating water. These pipes may fundamentally be considered as U-tubes. Since the RCCS is critical in case of a Loss of Flow Accident (LOFA), it is very important to characterise the dynamics of such a system both for systems and control engineering purposes. Detailed Computational Fluid Dynamic (CFD) models do exist, but these models are too complex for the purposes mentioned. The majority of thermohydraulic simulation codes utilise a network approach towards representing thermohydraulic systems. This concept is used to extract state space models from graphic representations of such systems. The purpose of this paper is to illustrate the application of a State Space Model Extraction (SSME) method applied to a fundamental thermohydraulic system, namely a U-tube. The solution of the extracted state space model is compared with a validated systems CFD code called Flownex and shows good correlation.