1.1. Industrial Internet of Things (IIoT)
The Industrial Internet of Things (IIoT) [1
] defines the same principles as the Internet of Things (IoT) concept but applies them to industry instead of the consumer, general-purpose approach of IoT. IIoT’s main goals are to generate interoperability of physical objects and to improve system functioning by elaborating optimizing strategies through interoperation between different industrial entities, data analyses, and learning. Improved interfacing and communication brought by IIoT to the industry enables interoperation and may increase the intelligence of systems, thus allowing to significantly improve technological processes. Water industry-specific structures and functioning provide the perfect environment for improvements in efficiency, quality, and availability using Industry 4.0/IIoT principles. The water industry is represented by highly heterogeneous and geographically dispersed processes and technical solutions. These include legacy systems and new structures that are in stringent need of connecting the digital and the physical worlds in the context of highly functional process dependencies without interoperation. The current transition towards the IIoT paradigm is stimulated by the benefits that lie ahead such as cost reduction and increases in safety, productivity, and availability. This transition is also revealing a series of problems for the water industry. Specific to drinking water facilities, these issues include: water source quality changes, high energy and substance consumptions in the treatment process, and maintenance. Under the IIoT paradigm, the fog computing concept is emerging and becoming more significant in industry. This new term defines solutions that are placed closer to local automation and, therefore, are much more accessible and reliable.
IIoT is currently one of the most important research and development topics; it manages to draw significant attention from both academia and industry [2
]. This new paradigm is steering the industry towards more intelligent communication between different industrial entities by connecting computers, controllers, actuators, and sensors to the Internet [3
]. This better-connected industrial environment allows for superior information exchange between all the components involved [4
]. Consequently, the IIoT paradigm is facilitating the development of more sophisticated technical solutions and autonomous software algorithms [5
] that can improve the working characteristics (energy consumption, time efficiency) for industrial processes (see [8
Identifying data dependencies is a process of analyzing stored data and discovering relations and dependencies between the characteristics/tags that have their values stored. This process is vital for developing a proactive Historian application because it is necessary to understand the correct ways to react and adjust the system. In order to adjust technical system working parameters, an understanding of how the potential adjustment impacts the entire system (the rest of the working parameters) is required. For example, if working parameter A is adjusted by the proactive Historian, the application must know if parameter B is related or dependent on parameter A. Lack of information regarding data dependencies can provide the possibility that the proactive Historian applies adjustments that make the technical system unstable. So, data dependency identification is crucial for the rest of the processes that follow inside a proactive Historian application.
Because of the heterogeneity of devices that are starting to be connected under the IIoT paradigm [1
], there are many communication protocols used in the industry; research from [12
] provides insight on this problem. However, in recent years, Open Protocol Communication Unified Architecture (OPC UA) has started to become the standard IIoT protocol (see [13
]). The popularity of OPC UA is also sustained by the large number of available software development kits, which makes OPC UA-based development easy (see [17
1.2. Interoperability and Historian
The water industry contains a very large variety of systems/solutions. These solutions are also highly dispersed chronologically and by location. The authors presented in [20
] solutions for OPC UA wrapping with a high technology readiness level (TRL) applied at water distribution companies. The solutions led to interoperability.
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
]). 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.
Emergence of data accumulation leads to a different view from the IIoT perspective. Currently, data gathering in industry is usually implemented with Historian applications placed at the supervisory control and data acquisition (SCADA) level. The need of Historian applications in the water industry is emphasized by the research from [26
], which also proposes standardized directions for different types of water industry-specific objectives.
Currently, most Historian applications available for industry are still offered by well-known automation/SCADA software producers and are very expensive. Therefore, they are placed only at the top supervisory levels. However, recent research has proposed different approaches for Historians.
], the authors proposed an improved Historian structure that could handle large amounts of data. However, they used International Electrotechnical Commission 61850 protocol, which is an electricity domain-specific protocol, thus making the Historian not suitable for other industries.
A more general approach is presented in [28
], where the authors proposed a low-cost and lightweight Historian based on OPC UA, which made it potentially available for a wide range of industries. The solution embedded Node-RED into a Java application and stored the collected data inside an SQLite database. At the same time, it was a platform-independent and complete hardware/software solution. The proposed Historian application was successfully applied in the water industry.
], the authors proposed a distributed Historian framework, which allowed configuration of a Historian application by using an organizational model of a hierarchical system.
On a slightly different note, research from [30
] presented an efficient data archiving method designed specifically for storing historical sensor data.
Because of progress recently made in data accumulation, new opportunities are arising regarding usage of the collected data [31
]. This data can be used as input for software algorithms that can run autonomously and eventually optimize the technical systems from where the data was collected. This kind of software algorithm can bring great benefit to the industry by reducing costs and improving efficiency of various technical systems. There are numerous possible development directions in the stored data analysis area, but few researchers are currently integrating the IIoT context.
1.3. Towards a Proactive Historian Application in the Water Industry
Practical implementations resulting from this research paper were considered and deployed in the water industry. The water industry currently needs improvement in system functioning that cannot be obtained by most manual analyses of available data in the context of currently deployed data gathering solutions and structuring because of several issues, which are briefly detailed as follows: there is a large geographical spread of systems in this industry; Historian applications are currently available only at the top supervisory level because of the high costs that a classic Historian solution implies; collected data are filtered according to the hierarchical level of vertical integration and the implicit local process understanding level (a data processing operation closer to the technical systems (fog computing) would enable cost reduction and efficiency improvements); there is lack of significant pattern identification capabilities that are adaptable to the highly heterogenous water industry processes; there is lack of a process-aware Historian; and there is lack of proactive solutions that identify an applicable recipe and react over the local process in the form of corrective actions.
The proposed solution is conceived according to Industry 4.0 principles by offering superior connectivity and flexibility; therefore, it is applicable to the wide variety of local systems in the water industry. This is achieved by using the Node-RED platform [33
], which offers various nodes that enable interfacing local industrial systems (e.g., OPC UA, Modbus (TCP and serial), S7, TCP/IP Ethernet, etc.) and offer the possibility to add other interfacing nodes in case of a nonpopular protocol. The proposed solution can connect (gather data and also to react) to local and regional SCADA systems, and also to programmable logic controllers (PLCs)/human machine interfaces (HMIs)/gateways, offering a low-cost alternative to the current Historian applications available.
The water industry requires data accumulation and optimization techniques based on knowledge from gathered data to increase functioning efficiency. Studies need to be industry focused and applicable because various research findings have been purely theoretical without a chance to connect or to apply them on real water facilities. Drinking water treatment and distribution are critical processes with many parameters, and improvements in functioning are necessary. The treatment process is intensively studied, and in [34
] the authors proved that the treatment process itself determined the impact that climate changes had over the drinking water systems. Water quality indicators are also of high importance, and various methods are studied to implement their increase. Efficiency of the entire process relies also on the cost aspects. The cost issue is intensively analyzed and related to certain parameters (e.g., consumption of substances, energy, and maintenance costs). In [35
], the authors proved the efficiency of substance usage over water quality indicators. Some studies (see [36
]) present a fuzzy solution to determine water quality, and it would be interesting to observe if the method will be adopted by water distribution companies. The cost issue is studied in [37
] but without a specific concern over the water sources and the treatment process. Some studies focused on the cost issue considering automation techniques. For example, proof of reducing costs was provided in [38
] when pumps were used with frequency converters. This also impacted water sources and proved that the optimal solution for water wells was for them to be equipped with a pump that had frequency converters, which, therefore, contained local flow and leveled referenced closed-loop control algorithms. A very complex study regarding cost perception was provided [39
]. The cost was presented from various perspectives including the impact of parameters and equipment evaluation on the cost (e.g., energy consumption of equipment, equipment faults, maintenance costs, etc.), optimizing techniques in the water distribution network, etc. But, the study from [39
] was focused on the distribution network, and it did not deal with the water treatment process. In the same context, authors in [40
] presented a strategy to optimize reservoir functioning to minimize economic losses caused by pollution, and authors in [41
] detailed an energy consumption reduction conceptual solution and its impact on swimming pool water distribution. Another important issue was presented in [42
] regarding the influence of raw water over the treatment process. The study from [43
] showed water quality degradation for drinking water sources in a complex and long-term study (441 water supply systems using 18 years of data). The outcome of [43
] was relevant to the current paper’s research perspective: it was necessary for water quality of each water source to be determined considering that, in practice, there are no quality sensors on a water well, and all information must be derived from complex monitoring, analyzing, and learning procedures. The importance of data gathering, analysis, and learning is presented in several studies. In [44
], turbidity levels of the water sources were predicted using data mining techniques, and an early warning system was implemented. In [45
], a burst detection in metering areas of water distribution was presented based on functional patterns of water demand with supervised learning. The same type of research was developed in [46
], where based on SCADA, bursts were detected over a long-distance network. As data are highly important in quantitative research, in [47
], the authors presented a solution to impute missing data for water distribution systems.
In the context of water resource management, various issues are considered and analyzed. Using hydraulic modelling, research from [48
] studied hydraulic regimes using different datasets associated to flood and low-flow events. The study from [49
] presented a historical review of the evolution and problems of water sources and distribution. In [50
], authors presented the importance of meteorological data integration in the water domain. In [51
], the seepage effect on river dikes was investigated, which could affect the quality and availability of surface of water sources. Through IIoT, the impacts of the encountered problems may be reduced, or even eliminated, while the functioning of respective water treatment and distribution processes may be improved.
Following the above-mentioned status regarding IIoT, Historians, and the water industry, the next question arises: how do we efficiently use the accumulated amount of information to obtain maximum benefits for the water industry?
Data accumulation refers to Historians in automation and SCADA domains. An industry-oriented analysis, which considered both literature study and the authors’ many years of experience in the water industry, concluded that classic Historian solutions from automation-/SCADA-producing companies were: very expensive and therefore placed only at the top supervisory level (less than 5% of the encountered SCADA control rooms had separate Historians); used only for data accumulation and reporting purposes; provided only manual (Excel-type) data manipulation possibilities; were mostly unusable by operators because of process and application understanding issues; and were, many times, platform-dependent. No Historian was encountered in the water industry that used accumulated data to automatically identify data dependencies, that elaborated on optimizing strategy-related conclusions, nor that reacted over local process controls to increase its efficiency in any way. The currently implemented Historians in the water industry were not aware of interfaced process characteristics; therefore, they cannot have any process-related objective [52
Industry requirements from Industry 4.0, and implicitly from IIoT and industrial automation, include advantages such as cost reduction, improved safety, wider availability, and an increase in productivity. Interoperability is essential and it is dependent on equipment and automation/SCADA solutions. Further research towards interoperation, data analysis and pattern identification, objective function definition, and model-based analysis is process-dependent. Water distribution companies are concerned about cost reduction (energy efficiency, substance consumption, and good maintenance strategy) but also about water quality. The water industry includes various geographically and chronologically dispersed local processes and implementations. Consequently, the best solutions should be fog-based so they can be close to local automation, noninvasive over existing control solutions, and adaptable to the individual processes and control procedures in order to identify dependencies by analyzing and understanding gathered data and to react through algorithms that augment existing structures. No such solution was encountered in practice nor in the literature.
Therefore, the general objective of the initiated research is to answer the above-mentioned question by providing an approach that will increase industrial efficiency through data accumulation and analysis. The current paper proposes a reference architecture for a proactive Historian that consists of a multilevel algorithm hierarchy. The proposed architecture facilitates creation of an autonomous, proactive Historian able to optimize and influence a functional system without human assistance. Extending the research from [28
], where a basic low-cost Historian was developed, the current research presents a stored data analysis algorithm that identifies data dependencies between measured characteristic and reference characteristic, establishes degrees of dependency, and exposes functional patterns. The proposed solution is aware of the process, so that computing or process-related degrees of freedom are considered (e.g., parameter and functional limitations, output possibilities). The associated process is from the water industry, a domain where physical and digital entities are currently trying to find common ground in the context of Industry 4.0 (e.g., SC5-11-2018 Horizon 2020 European Commission research project call from 2018: “Digital solutions for water: linking the physical and digital world for water solutions”). Considering drinking water treatment and distribution processes, the specific objectives of this research are to provide the reference architecture for the Historian, the data dependency identification algorithm, degrees of dependency between characteristics, process awareness, interoperability and interoperation possibilities, an integrated solution that is applied and tested on a real system, and a step-ahead view in increasing the energy efficiency by improved water source manipulation.
The conceived research is applicable to any industry as long as the reference tags, the limitations, and the objectives are defined at the beginning so that the Historian will understand the purpose and the degrees of freedom of the analysis.
The following section presents processes that take place inside a typical drinking water treatment plant (DWTP), presents the proposed proactive Historian reference architecture, and provides an algorithm description. Section 3
details integration of the algorithm into the Historian application developed in [28
], illustrates test scenarios from the water industry, and provides insight into improving a DWTP. Section 4
discusses the results and findings, while Section 5
concludes this paper.