Identifying Data Dependencies as First Step to Obtain a Proactive Historian: Test Scenario in the Water Industry 4.0
Abstract
:1. Introduction
1.1. Industrial Internet of Things (IIoT)
1.2. Interoperability and Historian
1.3. Towards a Proactive Historian Application in the Water Industry
2. Materials and Methods
2.1. Drinking Water Treatment Plant (DWTP) Typical Processes
- Growth of water acidity/alkalinity (pH) and lowering conductivity levels are implicitly assured through energy and chlorine consumption (e.g., aeration blowers, chlorine station, and maintenance of charcoal filters).
- A high turbidity level implicates possible clogging, respectively high energy consumption and water losses, which result from cleaning the filters with air and water.
- A level control loop in the water distribution tank complemented with a flow-based control loop, which calls water sources according to the actual flow demand from the water distribution network, assures a very useful, anticipative character of the entire water distribution when high water consumption is identified in critical periods of a day. At night, however, water sources are usually stopped when the upper hysteresis limit is achieved in the water tank. If water losses are present in the water distribution network, then the flow control loop will activate water sources. If the flow setpoints in the flow-based closed-loop control algorithms at the water sources are too high, or are set at fixed values, the water source pumps may start and stop multiple times during the night, causing pump and water source wear out.
- Besides the previously mentioned problem, successive starting and stopping of the water sources causes activation/deactivation of the entire DWTP for short periods. This leads to perturbed filtering and chlorination processes.
- Water sources have different characteristics; therefore, some of them may provide higher flow values and some better water quality. Monitoring residual chlorine, blower functioning times, and filter washing cycles over longer periods of time, together with chosen water sources that are currently functioning (provided water flow values), water source quality indicators can be identified. Using suitable water sources, specific consumptions can be reduced (flow distribution).
- Water source quality indicators change over time.
- The water level in the water distribution tank cannot be kept inside two hysteresis limits because water consumption variation in the distribution network perturbs the level control algorithm. Consequently, inconsistencies of water reserves in the tank may be identified including higher energy consumption and possible water treatment process disturbances.
- Proper equipment functioning hours and number of starts is essential to consider because maintenance/replacement is expensive.
2.2. The Reference Architecture
2.3. The Implemented Solution—Algorithm Description
- Quantity information is 100%—indicates a 1:1 ratio between the analyzed characteristic and the reference, meaning that if the reference value changes by 20%, then the analyzed characteristic value also changes by 20%.
- Quantity information is 50%–150%—if the reference value changes by 20%, then the analyzed characteristic value changes by 10%–30%.
3. Results
3.1. Water Industry Application
- Water wells S7-314 type PLCs were all integrated in the S7-315 PLC, which was responsible for water distribution using the S7 protocol. Level- and flow-based control loops that were responsible for automatic water requests from the wells were implemented in the S7-315. Local flow-based control loops were implemented at each S7-314 PLC.
- The entire water treatment process was guided by two redundant S7-412-5H type PLCs.
- The S7-315 PLC responsible for water distribution and the redundant PLCs responsible for the water treatment process were integrated in the WinCC 7.2-based SCADA system, which consisted of two redundant servers and two clients. Since connectivity packs were configured at each server, OPC UA servers were available and assured interoperability.
- The solution was implemented on a Raspberry Pi 3 B (because of its reduced physical dimensions and the availability of industrial cases, which makes it suitable for industrial environments) using the Node-RED environment and an embedded Java application. The OPC UA client (several nodes were used: OpcUa-Browser, OpcUa-Client, TCP, etc.) was used to interface the entire system for data gathering and for noninvasive interventions over the local process. Having a complete local redundancy for the water treatment control structures, the Historian connection to the SCADA system was enough, but the S7 node was prepared for backup interfacing in case of a total SCADA failure. S7 protocol allowed for the current study to interface all PLCs. Details about the already developed Historian application can be found in [22]. This application was used as a starting point, to which the data dependencies identification algorithm was added.
- An OPC UA server (Node-RED flow was created to define the OPC UA folder/tag structuring inside the secured endpoint in order to constantly populate the address space with values and to propagate an eventual tag change) was implemented to assure higher-level interoperability of the Historian.
3.2. Test Scenario
3.3. One Step Further to Improve a Drinking Water Facility
- After analyzing the accumulated data using the presented solution, patterns were identified, and quality indicators for the water sources were conceived and set.
- Other research activity results (not published yet) were used to convert the water source quality indicators and the functioning hours into priorities that influenced the requested amount of water from each source in order to optimize water treatment. According to the determined priorities for the water sources, flow references were calculated and considered for each well.
- DWTP output water quality indicators that were kept inside limits: pH, conductivity, and turbidity.
- Overall energy consumption was the cost reduction objective.
- Chlorine consumption was kept under a limit.
- Filters were washed no more than 1 cycle/filter/day.
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Nicolae, A.; Korodi, A.; Silea, I. Identifying Data Dependencies as First Step to Obtain a Proactive Historian: Test Scenario in the Water Industry 4.0. Water 2019, 11, 1144. https://doi.org/10.3390/w11061144
Nicolae A, Korodi A, Silea I. Identifying Data Dependencies as First Step to Obtain a Proactive Historian: Test Scenario in the Water Industry 4.0. Water. 2019; 11(6):1144. https://doi.org/10.3390/w11061144
Chicago/Turabian StyleNicolae, Andrei, Adrian Korodi, and Ioan Silea. 2019. "Identifying Data Dependencies as First Step to Obtain a Proactive Historian: Test Scenario in the Water Industry 4.0" Water 11, no. 6: 1144. https://doi.org/10.3390/w11061144