The Study Of Dynamic Response Using ARX Model In Extraction Process

This work presents a model using system identification approach namely as ARX to represent the dynamic response for essential oil extraction process. A fresh set of data under feed in disturbance was collected using MATLAB Simulink. The 3000 samples of data was collected by using PRBS as an input and temperature in oC as an output. The collected data was separated into two groups; training data and estimation data by using interlacing technique. The model estimation was done by using linear regression method. The robustness of the model was evaluated by using best fit (R2), OSA, root mean square error (RMSE), correlation analysis and residual analysis (histogram). Based on validation results, the ARX model was successfully capturing the dynamic response of extraction process by provide the high best fit, low RMSE error and normally distributed by producing small mean and variance

A Multi-Parameter Empirical Model For Mesophilic Anaerobic Digestion

Anaerobic digestion, which is the process by which bacteria breakdown organic matter to produce biogas (renewable energy source) and digestate (biofertiliser) in the absence of oxygen, proves to be the ideal concept not only for sustainable energy provision but also for effective organic waste management. However, the production amount of biogas to keep up with the global demand is limited by the underperformance in the system implementing the AD process. This underperformance is due to the difficulty in obtaining and maintaining the optimal operating parameters/states for anaerobic bacteria to thrive with regards to attaining a specific critical population number, which results in maximising the biogas production. This problem continues to exist as a result of insufficient knowledge of the interactions between the operating parameters and bacterial community. In addition, the lack of sufficient knowledge of the composition of bacterial groups that varies with changes in the operating parameters such as temperature, substrate and retention time. Without sufficient knowledge of the overall impact of the physico-environmental operating parameters on anaerobic bacterial growth and composition, significant improvement of biogas production may be difficult to attain. In order to mitigate this problem, this study has presented a nonlinear multi-parameter system modelling of mesophilic AD. It utilised raw data sets generated from laboratory experimentation of the influence of four operating parameters, temperature, pH, mixing speed and pressure on biogas and methane production, signifying that this is a multiple input single output (MISO) system. Due to the nonlinear characteristics of the data, the nonlinear black-box modelling technique is applied. The modelling is performed in MATLAB through System Identification approach. Two nonlinear model structures, autoregressive with exogenous input (NARX) and Hammerstein-Wiener (NLHW) with different nonlinearity estimators and model orders are chosen by trial and error and utilised to estimate the models. The performance of the models is determined by comparing the simulated outputs of the estimated models and the output in the validation data. The approach is used to validate the estimated models by checking how well the simulated output of the models fits the measured output. The best models for biogas and methane production are chosen by comparing the outputs of the best NARX and NLHW models (each for biogas and methane production), and the validation data, as well as utilising the Akaike information criterion to measure the quality of each model relative to each of the other models. The NLHW models mhw2 and mhws2 are chosen for biogas and methane production, respectively. The identified NLHW models mhw2 and mhws2 represent the behaviour of the production of biogas and methane, respectively, from mesophilic AD. Among all the candidate models studied, the nonlinear models provide a superior reproduction of the experimental data over the whole analysed period. Furthermore, the models constructed in this study cannot be used for scale-up purpose because they are not able to satisfy the rules and criteria for applying dimensional analysis to scale-up

Object Detection and Tracking in Cooperative Multi-Robot Transportation

Contemporary manufacturing systems imply the utilization of autonomous robotic systems, mainly for the execution of manipulation and transportation tasks. With a goal to reduce transportation and manipulation time, improve efficiency, and achieve flexibility of intelligent manufacturing systems, two or more intelligent mobile robots can be exploited. Such multi-robot systems require coordination and some level of communication between heterogeneous or homogeneous robotic systems. In this paper, we propose the utilization of two heterogeneous robotic systems, original intelligent mobile robots RAICO (Robot with Artificial Intelligence based COgnition) and DOMINO (Deep learning-based Omnidirectional Mobile robot with Intelligent cOntrol), for transportation tasks within a laboratory model of a manufacturing environment. In order to reach an adequate cooperation level and avoid collision while moving along predefined paths, our own developed intelligent mobile robots RAICO and DOMINO will communicate their current poses, and object detection and tracking system is developed. A stereo vision system equipped with two parallelly placed industrial-grade cameras is used for image acquisition, while convolutional neural networks are utilized for object detection, classification, and tracking. The proposed object detection and tracking system enables real-time tracking of another mobile robot within the same manufacturing environment. Furthermore, continuous information about mobile robot poses and the size of the bounding box generated by the convolutional neural network in the process of detection of another mobile robot is used for estimation of object movement and collision avoidance. Mobile robot localization through time is performed based on kinematic models of two intelligent mobile robots, and conducted experiments within a laboratory model of manufacturing environment confirm the applicability of the proposed framework for object detection and collision avoidance