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

AI for Improving the Overall Equipment Efficiency in Manufacturing Industry

Industry 4.0 has emerged as the perfect scenario for boosting the application of novel artificial intelligence (AI) and machine learning (ML) solutions to industrial process monitoring and optimization. One of the key elements on this new industrial revolution is the hatching of massive process monitoring data, enabled by the cyber-physical systems (CPS) distributed along the manufacturing processes, the proliferation of hybrid Internet of Things (IoT) architectures supported by polyglot data repositories, and big (small) data analytics capabilities. Industry 4.0 paradigm is data-driven, where the smart exploitation of data is providing a large set of competitive advantages impacting productivity, quality, and efficiency key performance indicators (KPIs). Overall equipment efficiency (OEE) has emerged as the target KPI for most manufacturing industries due to the fact that considers three key indicators: availability, quality, and performance. This chapter describes how different AI and ML solutions can enable a big step forward in industrial process control, focusing on OEE impact illustrated by means of real use cases and research project results

Industrial cyber physical systems supported by distributed advanced data analytics

The industry digitization is transforming its business models, organizational structures and operations, mainly promoted by the advances and the mass utilization of smart methods, devices and products, being leveraged by initiatives like Industrie 4.0. In this context, the data is a valuable asset that can support the smart factory features through the use of Big Data and advanced analytics approaches. In order to address such requirements and related challenges, Cyber Physical Systems (CPS) promote the development of more intelligent, adaptable and responsiveness supervisory and control systems capable to overcome the inherent complexity and dynamics of industrial environments. In this context, this work presents an agent-based industrial CPS, where agents are endowed with data analysis capabilities for distributed, collaborative and adaptive process supervision and control. Additionally, to address the different industrial levels’ requirements, this work combines two main data analysis scopes: at operational level, applying distributed data stream analysis for rapid response monitoring and control, and at supervisory level, applying big data analysis for decision-making, planning and optimization. Some experiments have been performed in the context of an electric micro grid where agents were able to perform distributed data analysis to predict the renewable energy production.info:eu-repo/semantics/publishedVersio

Big Data Analytics for Wireless and Wired Network Design: A Survey

Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks’ control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks

Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process

This paper outlines real-world control challenges faced by modern-day biopharmaceutical facilities through the extension of a previously developed industrial-scale penicillin fermentation simulation (IndPenSim). The extensions include the addition of a simulated Raman spectroscopy device for the purpose of developing, evaluating and implementation of advanced and innovative control solutions applicable to biotechnology facilities. IndPenSim can be operated in fixed or operator controlled mode and generates all the available on-line, off-line and Raman spectra for each batch. The capabilities of IndPenSim were initially demonstrated through the implementation of a QbD methodology utilising the three stages of the PAT framework. Furthermore, IndPenSim evaluated a fault detection algorithm to detect process faults occurring on different batches recorded throughout a yearly campaign. The simulator and all data presented here are available to download at www.industrialpenicillinsimulation.com and acts as a benchmark for researchers to analyse, improve and optimise the current control strategy implemented on this facility. Additionally, a highly valuable data resource containing 100 batches with all available process and Raman spectroscopy measurements is freely available to download. This data is highly suitable for the development of big data analytics, machine learning (ML) or artificial intelligence (AI) algorithms applicable to the biopharmaceutical industry

Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process

This paper outlines real-world control challenges faced by modern-day biopharmaceutical facilities through the extension of a previously developed industrial-scale penicillin fermentation simulation (IndPenSim). The extensions include the addition of a simulated Raman spectroscopy device for the purpose of developing, evaluating and implementation of advanced and innovative control solutions applicable to biotechnology facilities. IndPenSim can be operated in fixed or operator controlled mode and generates all the available on-line, off-line and Raman spectra for each batch. The capabilities of IndPenSim were initially demonstrated through the implementation of a QbD methodology utilising the three stages of the PAT framework. Furthermore, IndPenSim evaluated a fault detection algorithm to detect process faults occurring on different batches recorded throughout a yearly campaign. The simulator and all data presented here are available to download at www.industrialpenicillinsimulation.com and acts as a benchmark for researchers to analyse, improve and optimise the current control strategy implemented on this facility. Additionally, a highly valuable data resource containing 100 batches with all available process and Raman spectroscopy measurements is freely available to download. This data is highly suitable for the development of big data analytics, machine learning (ML) or artificial intelligence (AI) algorithms applicable to the biopharmaceutical industry

Decision Support System for Improved Operations, Maintenance, and Safety: a Data-Driven Approach

With industry 4.0, a new era of the industrial revolution with a focus on automation, inter-connectivity, machine learning, and real-time data collection and analysis are emerging. The smart digital technology which includes smart sensors, data acquisition, processing, and control based on big data, machine learning, and Artificial Intelligence (AI) provides boundless opportunities for the end-users to operate their plants under more optimized, reliable, and safer conditions. During an abnormal event in an industrial facility, operators are inundated with information to infer and act. Hence, there is a critical need to develop solutions that assist operators during such critical events. Also, because of the obsolescence challenges of typical industrial control systems, a new paradigm of Open Process Automation (OPA) is emerging. OPA requires a Real-time Operational Technology (OT) services to analyze the data generated by the sensors and control loops to assist the process plant operations by developing applications for advanced computing platforms in open source software platforms. The aim of this research is to highlight the potential applications of big data analytics, machine learning, and AI methods and develop solutions for plant operation, maintenance, process safety and risk management for real industry problems. This research work includes: 1. an alarm management framework integrated with data-driven (Key Performance Indicators) KPIs bench-marking, and a visualization tool is developed to address alarm management challenges; 2. a deep learning-based data-driven process fault detection and diagnosis method on cloud computing to identify abnormal process conditions; and 3. applications such as predictive maintenance, dynamic risk mapping, incident database analysis, application of Natural Language Processing (NLP) for text classification, and barrier assessment for dynamic risk mapping, A unified workflow approach is used to define the data-sources, applicable domains, and develop proposed applications. This work integrates data generated by field instrumentation, expert knowledge with data analytics and AI techniques to provide guidance to the operator or engineer to effectively take proactive decisions through “action-boards”. The robustness of the developed methods and algorithms is validated using real and simulated data sets. The proposed methods and results provide a future road map for any organization to deal with data integration with such applications leading to productive, safer and more reliable operations

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft