# Machine Learning-Supported Designing of Human-Machine Interfaces
## 4 Conclusions  
In this work, a machine learning-supported HMI designing method was proposed. The method aims to support the application of the _Navigation and Layout_ principle of the ISA101 standard, namely the _grouping related elements together_ aspect. Alarm signals, operator actions, and display actions from a hydrofluoric acid alkylation plant were analysed. The log file was partitioned based on frequent sequential patterns of the stored events. Process control models were explored with process mining tools. A network-like visualisation of a typical process enabled a comparison of the number of workflows and the number of displays in which the workflows are represented. A new, higher-level display was recommended on the basis of the models gained and the existing structure of the HMI displays. Even without deep knowledge of the system, the presented method proved effective. However, manual work is needed to evaluate the displays, which results in subjective decisions. The method can be upgraded to define objective KPIs that support solving the minimisation problem.  
In addition to data-driven HMI optimisation, there are other potential benefits to using the extracted knowledge. By comparing the actual online process data with formerly defined event scenarios, a semi- or fully automated online HAZOP analysis tool can be built. The gained process patterns can be compared to a reference model that allows the evaluation of the work quality of the operators and supports the development of the Operator Training System (OTS).  
Another potential future research direction is the implementation of machine learning in SCADA systems, which is an actual topic in the industry, as more and more solutions will be assisted by artificial intelligence. The proposed method is capable of processing acquired data and finding patterns among the event chains that lead to failures and the  
Figure 7: The fictive plant has five sections containing 14 pieces of equipment. The displayed content of the sections is defined by using the proposed method. The suggested alarm handling screen is created based on processed historical data.  
response actions of the operators. Using ML solutions enables the continuous dynamic and adaptive development of industrial process control systems.  
The topic of trend charts can also be an interesting area. With the help of ML-supported solutions, process-variable dependencies can be gained from historical data, adding a predictive function to the system. For example, if the operator calls a trend chart of a specific process variable, an intelligent system can offer other trend charts, which may be useful in the actual control activity.  
It is important to emphasise that ML-supported process control solutions do not aim to replace the human workforce. Using these methods and techniques shall result in safer and more efficient process operations by supporting decision-making with more process-aware human-machine interfaces.  
Conceptualization, J.A. and L.B.; methodology, L.B.; writing--original draft preparation, L.B.; writing--review and editing, J.A.; supervision, J.A. All authors have read and agreed to the published version of the manuscript.  
This work has been implemented by the OTKA 143482 (Monitoring Complex Systems by goal-oriented clustering algorithms) and the TKP2021-NVA-10 projects with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the 2021 Thematic Excellence Programme funding scheme.  
Not applicable.  
The following abbreviations are used in this manuscript:  
HMIHuman-Machine Interface.  
HP-HMIHigh-Performance Human-Machine Interface.  
MLMachine Learning.  
UXUser experience.  
UIUser interface.  
PLCProgrammable Logic Controller.  
P&IDPipping and Instrumentation Diagram.  
DCSDistributed Control System.  
SCADASupervisory Control and Data Acquisition.  
HAZOPHazard and Operability.  
OTSOperator Training System.