An innovative metaheuristic strategy for solar energy management through a neural networks framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development

An Insect-Inspired Target Tracking Mechanism for Autonomous Vehicles

Target tracking is a complicated task from an engineering perspective, especially where targets are small and seen against complex natural environments. Due to the high demand for robust target tracking algorithms a great deal of research has focused on this area. However, most engineering solutions developed for this purpose are often unreliable in real world conditions or too computationally expensive to be used in real-time applications. While engineering methods try to solve the problem of target detection and tracking by using high resolution input images, fast processors, with typically computationally expensive methods, a quick glance at nature provides evidence that practical real world solutions for target tracking exist. Many animals track targets for predation, territorial or mating purposes and with millions of years of evolution behind them, it seems reasonable to assume that these solutions are highly efficient. For instance, despite their low resolution compound eyes and tiny brains, many flying insects have evolved superb abilities to track targets in visual clutter even in the presence of other distracting stimuli, such as swarms of prey and conspecifics. The accessibility of the dragonfly for stable electrophysiological recordings makes this insect an ideal and tractable model system for investigating the neuronal correlates for complex tasks such as target pursuit. Studies on dragonflies identified and characterized a set of neurons likely to mediate target detection and pursuit referred to as ‘small target motion detector’ (STMD) neurons. These neurons are selective for tiny targets, are velocity-tuned, contrast-sensitive and respond robustly to targets even against the motion of background. These neurons have shown several high-order properties which can contribute to the dragonfly’s ability to robustly pursue prey with over a 97% success rate. These include the recent electrophysiological observations of response ‘facilitation’ (a slow build-up of response to targets that move on long, continuous trajectories) and ‘selective attention’, a competitive mechanism that selects one target from alternatives. In this thesis, I adopted a bio-inspired approach to develop a solution for the problem of target tracking and pursuit. Directly inspired by recent physiological breakthroughs in understanding the insect brain, I developed a closed-loop target tracking system that uses an active saccadic gaze fixation strategy inspired by insect pursuit. First, I tested this model in virtual world simulations using MATLAB/Simulink. The results of these simulations show robust performance of this insect-inspired model, achieving high prey capture success even within complex background clutter, low contrast and high relative speed of pursued prey. Additionally, these results show that inclusion of facilitation not only substantially improves success for even short-duration pursuits, it also enhances the ability to ‘attend’ to one target in the presence of distracters. This inspect-inspired system has a relatively simple image processing strategy compared to state-of-the-art trackers developed recently for computer vision applications. Traditional machine vision approaches incorporate elaborations to handle challenges and non-idealities in the natural environments such as local flicker and illumination changes, and non-smooth and non-linear target trajectories. Therefore, the question arises as whether this insect inspired tracker can match their performance when given similar challenges? I investigated this question by testing both the efficacy and efficiency of this insect-inspired model in open-loop, using a widely-used set of videos recorded under natural conditions. I directly compared the performance of this model with several state-of-the-art engineering algorithms using the same hardware, software environment and stimuli. This insect-inspired model exhibits robust performance in tracking small moving targets even in very challenging natural scenarios, outperforming the best of the engineered approaches. Furthermore, it operates more efficiently compared to the other approaches, in some cases dramatically so. Computer vision literature traditionally test target tracking algorithms only in open-loop. However, one of the main purposes for developing these algorithms is implementation in real-time robotic applications. Therefore, it is still unclear how these algorithms might perform in closed-loop real-world applications where inclusion of sensors and actuators on a physical robot results in additional latency which can affect the stability of the feedback process. Additionally, studies show that animals interact with the target by changing eye or body movements, which then modulate the visual inputs underlying the detection and selection task (via closed-loop feedback). This active vision system may be a key to exploiting visual information by the simple insect brain for complex tasks such as target tracking. Therefore, I implemented this insect-inspired model along with insect active vision in a robotic platform. I tested this robotic implementation both in indoor and outdoor environments against different challenges which exist in real-world conditions such as vibration, illumination variation, and distracting stimuli. The experimental results show that the robotic implementation is capable of handling these challenges and robustly pursuing a target even in highly challenging scenarios.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Inferential active disturbance rejection control of distillation columns

PhD ThesisThe distillation column is an important processing unit in the chemical and oil refining industry. Distillation is the most widely employed separation method in the world’s oil plants, chemical and petrochemical industrial facilities. The main drawback of the technique is high energy consumption, which leads to high production costs. Therefore, distillation columns are required to be controlled close to the desired steady state conditions because of economic incentives. Most industrial distillation columns are currently controlled by conventional multi-loop controllers such as proportional-integral-derivative (PID) controllers, which have several shortcomings such as difficulty coping with sudden set-point jumps, complications due to the integral term (I), and performance degradation due to the effect of noise on the derivative term (D). The control of ill-conditioned and strongly non-linear plants such as high purity distillation needs advanced control schemes for high control performance. This thesis investigates the use of active disturbance rejection control (ADRC) for product composition control in distillation columns. To the author’s knowledge, there are few reported applications of ADRC in the chemical industry. Most ADRC applications are in electrical, robotics and others. Therefore, this research will be the first to apply the ADRC scheme in a common chemical processing unit, and can be considered as a first contribution of this research. Initially, both PI and ADRC schemes are developed and implemented on the Wood–Berry distillation column transfer function model, on a simulated binary distillation column based on a detailed mechanistic model, and on a simulated heat integrated distillation column (HIDiC) based on a detailed mechanistic model. Process reaction curve method and system identification tools are used to obtain the 2×2 multi-input multi-output (MIMO) transfer function of both binary and HIDiC for the purpose of PI tuning where the biggest log-modulus tuning (BLT) method is used. Then, the control performance of ADRC is compared to that of the traditional PI control in terms of set-point tracking and disturbance rejection. The simulation result clearly indicates that the ADRC gives better control performance than PI control in all three case studies. The long time delay associated with product composition analysers in distillation columns such as gas chromatography deteriorates the overall control performance of the ADRC scheme. v To overcome this issue an inferential ADRC scheme is proposed and can be considered as a second contribution of this research. The tray temperatures of distillation columns are used to estimate both the top and bottom product compositions that are difficult to measure on-line without a time delay. Due to the strong correlation that exists in the tray temperature data, principal component regression (PCR) and partial least square (PLS) are used to build the soft sensors, which are then integrated into the ADRC. In order to overcome control offsets caused by the discrepancy between soft sensor estimation and actual compositions measurement, an intermittent mean updating technique is used to correct both the PCR and PLS model predictions. Furthermore, no significant differences were observed from the simulation results in the prediction errors reported by both PCR and PLS. The proposed inferential ADRC scheme shows effective and promising results in dealing with non-linear systems with a large measurement delay, where the ADRC has the ability to accommodate both internal uncertainties and external disturbances by treating the impact from both factors as total disturbances that will then be estimated using the extended state observer (ESO) and cancelled out by the control law. The inferential ADRC control scheme provides tighter product composition control that will lead to reduced energy consumption and hence increase the distillation profitability. A binary distillation column for separating a methanol–water mixture and an HIDiC for separating a benzene–toluene mixture are used to verify the developed inferential ADRC control scheme.Petroleum Development of Oman (PDO) for their generous support and scholarshi