Data based predictive control: Application to water distribution networks

In this thesis, the main goal is to propose novel data based predictive controllers to cope with complex industrial infrastructures such as water distribution networks. This sort of systems have several inputs and out- puts, complicate nonlinear dynamics, binary actuators and they are usually perturbed by disturbances and noise and require real-time control implemen- tation. The proposed controllers have to deal successfully with these issues while using the available information, such as past operation data of the process, or system properties as fading dynamics. To this end, the control strategies presented in this work follow a predic- tive control approach. The control action computed by the proposed data- driven strategies are obtained as the solution of an optimization problem that is similar in essence to those used in model predictive control (MPC) based on a cost function that determines the performance to be optimized. In the proposed approach however, the prediction model is substituted by an inference data based strategy, either to identify a model, an unknown control law or estimate the future cost of a given decision. As in MPC, the proposed strategies are based on a receding horizon implementation, which implies that the optimization problems considered have to be solved online. In order to obtain problems that can be solved e ciently, most of the strategies proposed in this thesis are based on direct weight optimization for ease of implementation and computational complexity reasons. Linear convex combination is a simple and strong tool in continuous domain and computational load associated with the constrained optimization problems generated by linear convex combination are relatively soft. This fact makes the proposed data based predictive approaches suitable to be used in real time applications. The proposed approaches selects the most adequate information (similar to the current situation according to output, state, input, disturbances,etc.), in particular, data which is close to the current state or situation of the system. Using local data can be interpreted as an implicit local linearisation of the system every time we solve the model-free data driven optimization problem. This implies that even though, model free data driven approaches presented in this thesis are based on linear theory, they can successfully deal with nonlinear systems because of the implicit information available in the database. Finally, a learning-based approach for robust predictive control design for multi-input multi-output (MIMO) linear systems is also presented, in which the effect of the estimation and measuring errors or the effect of unknown perturbations in large scale complex system is considered

Systematic Framework for Integration of Weather Data into Prediction Models for the Electric Grid Outage and Asset Management Applications

This paper describes a Weather Impact Model (WIM) capable of serving a variety of predictive applications ranging from real-time operation and day-ahead operation planning, to asset and outage management. The proposed model is capable of combining various weather parameters into different weather impact features of interest to a specific application. This work focuses on the development of a universal weather impacts model based on the logistic regression embedded in a Geographic Information System (GIS). It is capable of merging massive data sets from historical outage and weather data, to real-time weather forecast and network monitoring measurements, into a feature known as weather hazard probability. The examples of the outage and asset management applications are used to illustrate the model capabilities

Machine learning solutions for maintenance of power plants

The primary goal of this work is to present analysis of current market for predictive maintenance software solutions applicable to a generic coal/gas-fired thermal power plant, as well as to present a brief discussion on the related developments of the near future. This type of solutions is in essence an advanced condition monitoring technique, that is used to continuously monitor entire plants and detect sensor reading deviations via correlative calculations. This approach allows for malfunction forecasting well in advance to a malfunction itself and any possible unforeseen consequences. Predictive maintenance software solutions employ primitive artificial intelligence in the form of machine learning (ML) algorithms to provide early detection of signal deviation. Before analyzing existing ML based solutions, structure and theory behind the processes of coal/gas driven power plants is going to be discussed to emphasize the necessity of predictive maintenance for optimal and reliable operation. Subjects to be discussed are: basic theory (thermodynamics and electrodynamics), primary machinery types, automation systems and data transmission, typical faults and condition monitoring techniques that are also often used in tandem with ML. Additionally, the basic theory on the main machine learning techniques related to malfunction prediction is going to be briefly presented

Data Processing for IoT in Oil and Gas Refineries

This paper summarizes and gives examples of the using of IoT in Industry 4.0, especially in Oil and Gas Refineries. Industry 4.0 and Industrial Internet of Things (IIoT) technologies are driving digitalization driven by software and data solutions in many areas, particularly in industrial automation and manufacturing systems. Global refineries are currently all heavily instrumented, and process regulated in real-time to the millisecond. To meet the ever-increasing needs of operational demands, SCADA, Distributed Control Systems and Programmable Logic Controllers (DCS & PLCs) have grown significantly. On the other hand, certain assets and operations in a refinery are still not being monitored or evaluated in real-time. If an error occurs that causes production to be hampered, the company must bear large losses even though production stops in just a matter of minutes. This is one of the reasons why the oil and gas sector is starting to implement the Internet of Things (IoT). The overall aim of this paper is to give and summarize several papers to provide solutions for a simple process monitoring system that would enable process operators to identify any sources of abnormality quickly and easily in the process. A system is being made so that it can be accessed and transmit data remotely via a computer network and will display conditions in real-time without being limited by distance, space, and time. This will allow all previously disconnected assets and processes to be linked and monitored in real-time in a simpler, cost-effective, and easy-to-implement manner

Worldwide Deployment of Predictive Asset Management at Air Liquide

TutorialAir Liquide launched an international program to monitor and assess equipment asset health, resulting in a positive step-change in availability and reliability worldwide. Using predictive analytics, potential asset failures may be identified and appropriate intervention planned. Intervention prior to failure averts a possible reliability incident, adverse customer impact, and costly emergency maintenance activitie

Predictive Maintenance in SCADA-Based Industries: A literature review

The purpose of this paper is to mapping and review what has been done on the topic of research on predictive maintenance in SCADA (Supervisory Control and Data Acquisition) based industries. In the research area of predictive maintenance, various methods for predicting damage or time to failure of a machine have been proposed and applied in various industries. This paper systematically categorizes predictive maintenance in SCADA-based industries research based on industry classifications according to ISIC (International Standard Industrial Classification of All Economic Activities). Furthermore, the research scope is explored its connection to the topics of Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Supervisory Control and Data Acquisition (SCADA). It is found that 81.5% of the research was conducted on the electricity, gas, steam, and air conditioning supply industries, 11.1% of research was conducted on the mining and quarrying industry, and 7.4% of the research conducted in the manufacturing industry. It is also found that 85.2% of studies used AI and ML, 18.5% of the studies used IoT, and 18.5% of research used AI/ML and IoT technology together

A Biased Review of Sociophysics

Various aspects of recent sociophysics research are shortly reviewed: Schelling model as an example for lack of interdisciplinary cooperation, opinion dynamics, combat, and citation statistics as an example for strong interdisciplinarity.Comment: 16 pages for J. Stat. Phys. including 2 figures and numerous reference

Hosting critical infrastructure services in the cloud environment considerations

Critical infrastructure technology vendors will inevitability take advantage of the benefits offered by the cloud computing paradigm. While this may offer improved performance and scalability, the associated security threats impede this progression. Hosting critical infrastructure services in the cloud environment may seem inane to some, but currently remote access to the control system over the internet is commonplace. This shares the same characteristics as cloud computing, i.e., on-demand access and resource pooling. There is a wealth of data used within critical infrastructure. There needs to be an assurance that the confidentiality, integrity and availability of this data remains. Authenticity and non-repudiation are also important security requirements for critical infrastructure systems. This paper provides an overview of critical infrastructure and the cloud computing relationship, whilst detailing security concerns and existing protection methods. Discussion on the direction of the area is presented, as is a survey of current protection methods and their weaknesses. Finally, we present our observation and our current research into hosting critical infrastructure services in the cloud environment, and the considerations for detecting cloud attacks. © 2015 Inderscience Enterprises Ltd

Manufacturing Process Optimization Using Edge Analytics

Most manufacturing plants contain some amount of time series sensor data – streams of values and time stamps. This data, however, isn’t useful with most types of analytics or machine learning for the purpose of process optimization. This thesis presents a novel and innovative solution to the problem using a software stack leveraging the Predix Complex Event Processing Engine (Edge Analytics) to condition the data, combined with RFID for serialization. Each step in the formation of the solution is documented, from connecting equipment to analyzing and ingesting data produced by the edge analytic. This solution was developed and piloted at the GE Grid Solutions plant in Clearwater, FL