Non-Invasive Control Solution inside Higher-Level OPC UA based Wrapper for Optimizing Groups of Wastewater Systems

Korodi, Adrian; Radu, M; Crisan, Ruben

doi:10.1109/etfa.2018.8502550

Cited by 9 publications

(9 citation statements)

References 26 publications

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“…For a significant connection with the industry, high technological readiness level, and the fog computing abilities, the hardware and software environments used for FDC were the same as in [42]. Figure 4 details the fog-based FDC solution in direct relation with the local water infrastructure on various protocols.…”

Section: Increasing Energy Efficiencymentioning

confidence: 99%

“…Regarding the wastewater treatment, Sandu et al present a numerical study in [40] that creates new water paths by introducing wall structures and thus ensuring that low velocity flows of water (which facilitates the appearance of a sedimentation process that disturbs the normal water treatment process) cannot form. Also, in the wastewater treatment domain, the study from [41] presented predictive control schemes and tackled the wastewater treatment stability and efficiency improvement problems, while paper [42] details an optimizing strategy regarding wastewater network and treatment plant implemented non-invasively using IIoT concepts over legacy systems.…”

Section: Introductionmentioning

confidence: 99%

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Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

et al. 2020

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The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or impact is. It is necessary for industrial systems, even legacy ones, to assure interoperability in the context of chronologically dispersed and currently functional solutions, respectively; the Open Platform Communications Unified Architecture (OPC UA) protocol is an essential requirement. Then, following data accumulation, the resulting process-aware strategies have to present learning capabilities, pattern identification, and conclusions to increase efficiency or safety. Finally, model-based analysis and decision and control procedures applied in a non-invasive manner over functioning systems close the optimizing loop. Drinking water facilities, as generally the entire water sector, are confronted with several issues in their functioning, with a high variety of implemented technologies. The solution to these problems is expected to create a more extensive connection between the physical and the digital worlds. Following previous research focused on data accumulation and data dependency analysis, the current paper aims to provide the next step in obtaining a proactive historian application and proposes a non-invasive decision and control solution in the context of the Industrial Internet of Things, meant to reduce energy consumption in a water treatment and distribution process. The solution is conceived for the fog computing concept to be close to local automation, and it is automatically adaptable to changes in the process’s main characteristics caused by various factors. The developments were applied to a water facility model realized for this purpose and on a real system. The results prove the efficiency of the concept.

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Section: Increasing Energy Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

et al. 2020

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“…The superior interoperability provided by the OPC UA in the IIoT context enables horizontal interoperation between systems placed at the same hierarchical level (see [22,23]). The benefits of interoperation are proved for the water industry in study [24] on a wastewater network, which started from a cascaded wastewater pumping station (WWPS) towards the wastewater treatment plant (WWTP). In a fog computing scenario, by using a noninvasive control algorithm and interoperation, the solution from [24] optimizes clogged pipes failures, WWPS blockages, and supplementary stormwater at the inlet of the WWTP.…”

Section: Interoperability and Historianmentioning

confidence: 99%

“…The benefits of interoperation are proved for the water industry in study [24] on a wastewater network, which started from a cascaded wastewater pumping station (WWPS) towards the wastewater treatment plant (WWTP). In a fog computing scenario, by using a noninvasive control algorithm and interoperation, the solution from [24] optimizes clogged pipes failures, WWPS blockages, and supplementary stormwater at the inlet of the WWTP. By using interoperability, a noninvasive approach over existing functioning structures, and data accumulation, the study from [25] also presents a solution to reduce energy consumption in a WWTP.…”

Section: Interoperability and Historianmentioning

confidence: 99%

Identifying Data Dependencies as First Step to Obtain a Proactive Historian: Test Scenario in the Water Industry 4.0

Nicolae

Korodi

Silea

2019

Water

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Current efforts towards achieving better connectivity and increasing intelligence in functioning of industrial processes are guided by the Industrial Internet of Things paradigm and implicitly stimulate occurrence of data accumulation. In recent years, several researchers and industrial products have presented Historian application solutions for data accumulation. The large amounts of data that are gathered by these Historians remains mostly unused or used only for reporting purposes. So far, Historians have been focused on connectivity, data manipulation possibilities, and sometimes on low-cost solutions in order to gain higher applicability or to integrate multiple SCADA servers (e.g. Siemens–WinCC, Schneider Electric – Vijeo Citect, IGSS, Wonderware, InduSoft Web Studio, Inductive Automation – Ignition, etc.), etc. Both literature and industry are currently unable to identify a Historian solution that functions in fog and efficiently applies and is built upon Industry 4.0 ideas. The future is to conceive a proactive Historian that is able to, besides gathering data, identify dependencies and patterns for particular processes and elaborate strategies to increase performance in order to provide feedback through corrective action on the functional system. Using available solutions, determining patterns by the Historian operator in the context of big data is a tremendous effort. The motivation of this research is provided by the currently unoptimized and partly inefficient systems in the water industry that can benefit from cost reduction and quality indicator improvements through IIoT concepts related to data processing and process adjustments. As the first part of more complex research to obtain a proactive Historian, the current paper wishes to propose a reference architecture and to address the issue of data dependency analyses as part of pattern identification structures. The conceptual approach targets a highly customizable solution considering the variety of industrial processes, but it also underlines basic software modules as generally applicable for the same reason. To prove the efficiency of the obtained solution in the context of real industrial processes, and their corresponding monitoring and control solutions, the paper presents a test scenario in the water industry.

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“…Applying IIoT and Industry 4.0 concepts in the industry, the interoperability of the entities has to be assured both vertically [6] and horizontally [7] regarding the hierarchical levels, meaning that sensing devices and actuators from the field level, as well as Programmable Logic Controllers (PLCs), microcontrollers, microcomputers from the control level, SCADA systems, historians from the fog or cloud, have to vehiculate data on the same level (e.g., PLC to PLC) or on different levels (e.g., frequency converter to PLC, PLC to SCADA, sensing device to historian).…”

Section: Introductionmentioning

confidence: 99%

Cryptographic Considerations for Automation and SCADA Systems Using Trusted Platform Modules

Tidrea

Korodi

Silea

2019

Sensors

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The increased number of cyber threats against the Supervisory Control and Data Acquisition (SCADA) and automation systems in the Industrial-Internet-of-Things (IIoT) and Industry 4.0 era has raised concerns in respect to the importance of securing critical infrastructures and manufacturing plants. The evolution towards interconnection and interoperability has expanded the vulnerabilities of these systems, especially in the context of the widely spread legacy standard protocols, by exposing the data to the outside network. After gaining access to the system data by launching a variety of attacks, an intruder can cause severe damage to the industrial process in place. Hence, this paper attempts to respond to the security issue caused by legacy structures using insecure communication protocols (e.g., Modbus TCP, DNP3, S7), presenting a different perspective focused on the capabilities of a trusted platform module (TPM). Furthermore, the intent is to assure the authenticity of the data transmitted between two entities on the same (horizontal interoperation) or different (vertical interoperation) hierarchical levels communicating through Modbus TCP protocol based on functionalities obtained by integrating trusted platform modules. From the experimental results perspective, the paper aims to show the advantages of integrating TPMs in automation/SCADA systems in terms of security. Two methods are proposed in order to assure the authenticity of the messages which are transmitted, respectively the study presents the measurements related to the increased time latency introduced due to the proposed concept.

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Non-Invasive Control Solution inside Higher-Level OPC UA based Wrapper for Optimizing Groups of Wastewater Systems

Cited by 9 publications

References 26 publications

Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

Identifying Data Dependencies as First Step to Obtain a Proactive Historian: Test Scenario in the Water Industry 4.0

Cryptographic Considerations for Automation and SCADA Systems Using Trusted Platform Modules

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