Monitoring the waste to energy plant using the latest AI methods and tools

Solid wastes for instance, municipal and industrial wastes present great environmental concerns and challenges all over the world. This has led to development of innovative waste-to-energy process technologies capable of handling different waste materials in a more sustainable and energy efficient manner. However, like in many other complex industrial process operations, waste-to-energy plants would require sophisticated process monitoring systems in order to realize very high overall plant efficiencies. Conventional data-driven statistical methods which include principal component analysis, partial least squares, multivariable linear regression and so forth, are normally applied in process monitoring. But recently, latest artificial intelligence (AI) methods in particular deep learning algorithms have demostrated remarkable performances in several important areas such as machine vision, natural language processing and pattern recognition. The new AI algorithms have gained increasing attention from the process industrial applications for instance in areas such as predictive product quality control and machine health monitoring. Moreover, the availability of big-data processing tools and cloud computing technologies further support the use of deep learning based algorithms for process monitoring. In this work, a process monitoring scheme based on the state-of-the-art artificial intelligence methods and cloud computing platforms is proposed for a waste-to-energy industrial use case. The monitoring scheme supports use of latest AI methods, laveraging big-data processing tools and taking advantage of available cloud computing platforms. Deep learning algorithms are able to describe non-linear, dynamic and high demensionality systems better than most conventional data-based process monitoring methods. Moreover, deep learning based methods are best suited for big-data analytics unlike traditional statistical machine learning methods which are less efficient. Furthermore, the proposed monitoring scheme emphasizes real-time process monitoring in addition to offline data analysis. To achieve this the monitoring scheme proposes use of big-data analytics software frameworks and tools such as Microsoft Azure stream analytics, Apache storm, Apache Spark, Hadoop and many others. The availability of open source in addition to proprietary cloud computing platforms, AI and big-data software tools, all support the realization of the proposed monitoring scheme

An End-to-End Big Data Analytics Platform for IoT-enabled Smart Factories: A Case Study of Battery Module Assembly System for Electric Vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and low-cost big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an Electric Vehicle (EV) battery module smart assembly automation system designed by the Automation Systems Group (ASG) at the University of Warwick, UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments

SmartFD: A Real Big Data Application for Electrical Fraud Detection

The main objective of this paper is the application of big data analytics to a real case in the field of smart electric networks. Smart meters are not only elements to measure consumption, but they also con stitute a network of millions of sensors in the electricity network. These sensors provide a huge amount of data that, once analyzed, can lead to significant advances for the society. In this way, tools are being developed in order to reach certain goals, such as obtaining a better consumption estimation (which would imply a better production planning), finding better rates based on the time discrimination or the contracted power, or minimizing the non-technical losses in the network, whose actual costs are eventually paid by end-consumers, among others. In this work, real data from Spanish consumers have been analyzed to detect fraud in con sumption. First, 1 TB of raw data was preprocessed in a HDFS-Spark infrastructure. Second, data duplication and outliers were removed, and missing values handled with specific big data algorithms. Third, cus tomers were characterized by means of clustering techniques in different scenarios. Finally, several key factors in fraud consumption were found. Very promising results were achieved, verging on 80% accuracyMinisterio de Economía y Competitividad TIN2014-55894-C2-RMinisterio de Economía y Competitividad TIN2017-88209-C2-

Open Source Big Data Platforms and Tools: An Analysis

Big data is attracting an excessive amount of interest in the IT and academic sectors. On a regular basis, computer and digital industries generate more data than they have space to store. In the current situation, five billion people have their own mobile phone, and over two billion people are linked globally to exchange various types of data. By 2020, it is estimated that about fifty billion people will be connected to the internet. During2020, data generation, use, and sharing would be forty-four times higher than in previous years. A variety of sectors and organizations are using big data to manage various operations. As a result, a thorough examination of big data's benefits, drawbacks, meaning, and characteristics is needed. The primary goal of this research is to gather information on the various open-source big data tools and platforms that are used by various organizations. In this paper we use a three perspective methodology to identify the strength and weaknesses of the workflow in a open source big data arena. This helps to establish a pipeline of workflow events for both researcher and entrepreneur decision making

Secure and Privacy Driven Energy Data Analytics

PhD thesis in Information technologyRenewable resources are the main energy sources in a smart grid project. In order to ensure the smooth functioning of the smart grid, Information and Communication Technologies (ICT) need to be utilised efficiently. The objective of the SmartNEM project is to effectively utilise the technologies such as Machine Learning, Blockchain and Data Hubs for the aforementioned purpose and at the same time ensure a secured and privacy preserved solution. The data involved in smart grids require high security and it can be sensitive due to the household data which contains personal information. The individuals can be reluctant to share these data due to mistrust and to avoid unnecessary manipulation of the data they provide. In order to overcome this it is necessary to build a trust based framework in which one could ensure data security and data privacy for the data owners to open up their data for data analysis. To achieves this we have proposed an architecture called TOTEM, Token for Controlled Computation, which integrates Blockchain and Big Data technologies. The conventional method of data analysis demands data be moved across the network to the location where the execution happens, however in the TOTEM architecture computational code will be moved to the data owner’s environment where the data is located. The TOTEM is a three layer architecture (Blockchain consortium layer, Storage layer and Computational layer) with two main actors, data provider and data consumer. Data provider provides metadata of the data they own and provide resources for the execution of data. Data consumers will get an opportunity to execute their own code on the data provider´s data. For a controlled computation and to avoid malicious functions an entity called totem is introduced in the architecture. The authorised users should meet the requirements of Totem value for executing their code on the requested data. For live monitoring of the totem value throughout the run time is achieved with the components such as totem manager and updaters in the computational layer. The code must follow a specific format and will undergo preliminary checks with the TOTEM defined SDK and smart contracts deployed by the data providers in the blockchain network. The Extended TOTEM architecture is also proposed to address the additional features when it is needed to combine the results from multiple data providers without sharing the data. This research work focused on the design of the TOTEM architecture and implementation as a proof of concept for the newly introduced components in the architecture. We have also introduced artificial intelligence in the framework to improve core features’ functionality. In the present research, the TOTEM architecture is proposed for the SmartNEM project to utilize the energy data for decision making and figure out the trends or patterns, while maintaining data privacy, data ownership, accountability and traceability. Moreover, the architecture can be extended to other domains such as health, education, etc, where data security and privacy is the key concern in sharing the data