Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels

Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft

Distributed model predictive control of steam/water loop in large scale ships

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Nonlinear predictive control applied to steam/water loop in large scale ships

In steam/water loop for large scale ships, there are mainly five sub-loops posing different dynamics in the complete process. When optimization is involved, it is necessary to select different prediction horizons for each loop. In this work, the effect of prediction horizon for Multiple-Input Multiple-Output (MIMO) system is studied. Firstly, Nonlinear Extended Prediction Self-Adaptive Controller (NEPSAC) is designed for the steam/water loop system. Secondly, different prediction horizons are simulated within the NEPSAC algorithm. Based on simulation results, we conclude that specific tuning of prediction horizons based on loop’s dynamic outperforms the case when a trade-off is made and a single valued prediction horizon is used for all the loops

Non-constructive interval simulation of dynamic systems

Publisher PD

Iterative nonlinear model predictive control of a PH reactor. A comparative analysis

IFAC WORLD CONGRESS (16) (16.2005.PRAGA, REPÚBLICA CHECA)This paper describes the control of a batch pH reactor by a nonlinear predictive controller that improves performance by using data of past batches. The control strategy combines the feedback features of a nonlinear predictive controller with the learning capabilities of run-to-run control. The inclusion of real-time data collected during the on-going batch run in addition to those from the past runs make the control strategy capable not only of eliminating repeated errors but also of responding to new disturbances that occur during the run. The paper uses these ideas to devise an integrated controller that increases the capabilities of Nonlinear Model Predictive Control (NMPC) with batch-wise learning. This controller tries to improve existing strategies by the use of a nonlinear controller devised along the last-run trajectory as well as by the inclusion of filters. A comparison with a similar controller based upon a linear model is performed. Simulation results are presented in order to illustrate performance improvements that can be achieved by the new method over the conventional iterative controllers. Although the controller is designed for discrete-time systems, it can be applied to stable continuous plants after discretization

Investigations into microgrid sizing and energy management strategies

PhD ThesisThe evolution of microgrids represents a significant step towards the transition to more sustainable power systems. Recent trends in microgrids include the integration of renewable energy resources (RERs), alternative energy resources (AERs) and energy storage systems (ESSs). However, the integration of these systems creates new challenges on microgrid operation because of their stochastic and intermittent nature. To mitigate these challenges, determining the appropriate size together with the best energy management strategy (EMS) systems are essential to ensure economic and optimal performance. This thesis presents an investigation into sizing and energy management of microgrids. In the first part of the thesis, an analytical and economic sizing (AES) approach is developed to find the optimal size of a grid-connected photovoltaicbattery energy storage system (PV-BESS). The proposed approach determines the optimal size based on the minimum levelised cost of energy (LCOE). Fundamental to this approach obtains an improved formula of LCOE which includes new parameters for reflecting the impact of surplus PV energy and the energy purchased from the grid. In the second part of this thesis, an integrated framework is proposed for finding the best size-EMS combination of a stand-alone hybrid energy system (HES). The HES consists of PV, BESS, diesel generator, fuel cell, electrolyser, and hydrogen tank. The proposed framework includes three consecutive steps; first, performing the AES to obtain the initial size of the HES, second, implementing the initial EMS using finite automata (FA) and instantiating multiple EMSs; and third, developing an evaluation model to assess the instantiated EMSs and extract the featured conditions to produce an improved EMS. Then the AES approach is re-exercised using the improved EMS to obtain the best size-EMS combination. The core of this framework is utilising FA to implement various EMSs and capturing the impact of selecting the best EMS on the sizing of the HES. Furthermore, a sensitivity analysis is performed to address the uncertainty in demand and solar radiation data showing their effect on the HES performance. The analysis is carried out by assuming variations in solar radiation and demand annual data. Several scenarios are generated from the sensitivity analysis, and a number of performance indices are computed for each scenario. Following that, a vii fuzzy logic controller is designed using the performance indices as fuzzy input sets. The objective of this controller is to modify the EMS obtained from the integrated framework. This can be accomplished by detecting any changes in the demand and solar radiation and accordingly modify the operating conditions of the diesel generator, fuel cell, and electrolyser. The performance of the proposed approaches is validated using real datasets for both demand and solar radiation. The results show the optimal size and EMS for both grid-connected and stand-alone microgrids. Moreover, the designed fuzzy logic controller enables the microgrid to mitigate the uncertainty in the demand and generation data. The proposed approaches can be used with various scales of microgrids to extract manifold benefits where reliability, environmental and cost requirements can not be tolerated.Applied Science Private University in Jorda

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science