Evaluating BPMN Extensions for Continuous Processes Based on Use Cases and Expert Interviews

The review targets literature at the intersection of continuous processes/process engineering and process technologies. Hence, the search terms focus on continuous processes refined by the process development and implementation phases. The search terms are then compiled and evaluated based on scientific databases Scopus⁴ and dblp.⁵ Selection criteria comprise general ones such as English language, public availability, # citations> 5 before 2017, spanning a time period since 2002, and selecting work where the term “continuous” refers to the process. From 700 hits overall, 401 papers were selected based on the criteria, read, and analyzed. Descriptions of the search strategy and the results and summaries can be found in the supplementary material⁶ of this work in Appendix E.

The analysis of the selected literature is based on a two-dimensional classification in order to identify “white spots” indicating open research challenges. The first dimension reflects the process life cycle, inspired by literature (Weske 2007; Leitner and Rinderle-Ma 2014), covering phases Design, Configuration, Enactment, Evaluation, and Change. We interpret these phases for continuous processes as follows: Continuous processes are regarded to be in their Design phase if the paper focuses on the development, modeling, simulation, and validation of continuous process models. The Configuration phase is entered as soon as the implementation of the process or the commissioning is described. The Enactment phase comprises the execution of a continuous process. This also includes the execution on laboratory scale process plants that can server as a blue print for industrial-scale applications. The continuous process is monitored at run-time and process data is collected. In the Evaluation phase, the collected process data is evaluated and analyzed. Publications fall into this category if additional insights are derived from the collected data using specific methods. Change classifies publications which describe an approach to improve a continuous process based on the preceding collection and analysis of process data.

The second dimension covers the thematic focus of the research, which is derived from the process mining perspectives as described in van der Aalst and Carmona (2022); Yasmin et al. (2018). The Control perspective is chosen for papers which evaluate different process flows to reach the same or a similar final product. An example is the comparison of a discrete or batch and an equivalent continuous process. Papers have been assigned to Data if the collected process data has been the research focus. The class Time marks papers that concentrate on events or time as a process parameter. Resources can be used as a label for publications, that focus on the involvement of technical equipment and operators.

The results of the literature search classified along the dimensions process life cycle and process perspective are displayed as bubble chart shown in Fig. 2. Regarding the life cycle, most papers focus on the Design of continuous processes, followed by Change, Enactment, and Evaluation. The least number of papers is assigned to the Configuration phase, although it refers to important topics such as commissioning of continuous process systems and assistance systems.

Regarding the process perspectives, most approaches focus on Data overall and across all life cycle phases. This differs from discrete processes where “[t]he control-flow description is the backbone of any process model” (van der Aalst et al. 2011) as it captures the behavior of a process. Also Time and Resources seem a bit under-represented given their significance for continuous processes driven by temporal constraint and requiring resources for their execution.

In order to analyze the combinations of life cycle phase and process perspective, Figs. 3 and 4 depict the relative numbers from the view point of the phases and the perspectives respectively. We can see that the data perspective plays an important role across all life cycle phases. This is not surprising, since process data is the basis for calculating and evaluating process performance and product quality. Monitoring process data gives insights into the state of the process and is therefore also relevant for safety. Resources seem important for the configuration phase and control-flow for the enactment phase, as well. The second observation can be also seen from the viewpoint of the perspectives where for control-flow the enactment phase seems even more important than the design phase which is “dominant” for the other perspectives. Aside design, for resources, the other life cycle phases seem equally important while for time and data, the change phase seems to be crucial. From this it can be interpreted that different operation modes for the same result were more often treated by experiments than by modeling and simulation in research. The execution of processes in a pilot or laboratory plant is more often implemented in a comparison of batch and continuous processes than just modeling and simulation.

Overall, we can conclude that applying process modeling and analysis techniques to continuous processes seems promising, in particular, to capture the process behavior through the control-flow perspective. Moreover, time and resources can emphasized as important perspectives that currently fall short behind the data perspective. From a life cycle phase point of view, configuration offers the most potential for further research, followed by enactment and evaluation.

The purpose of scientific workflows is the description of data processing steps in the context of a scientific process (Sadeghibogar et al. 2023). Scientific workflows deal with the uniform description of executing scientific methodologies in order to achieve traceability and reproducibility of results and use progressively emerging and iteratively improved models which are mainly focused on the transport and processing of data (Barker and Hemert 2007). Existing approaches distinguish scientific workflows from business processes along the dominance of data flow (scientific workflow) and control flow patterns (business process) (Ludäscher et al. 2006) and the flexibility in design (Gil et al. 2007). Based on the previous descriptions of processes in the manufacturing industry, overlaps with scientific workflows can be identified. The choice between the process types discrete and continuous based on certain characteristics in process engineering already reveals connections to scientific workflows. Among others, scientific workflows can be characterized using metrics such as execution runtime, requirements for inputs and outputs as well as resource management (Juve et al. 2013). These metrics conform to the previously mentioned decisive factors such as yield, input materials, products, and availability of resources. Following the approach of using closed-loop control as a foundation for modeling continuous processes further overlaps become apparent. With scientific workflows, the repetitive execution of processes with different sets of parameters, which have partly been modified based on findings from the previous cycle, are found as well as automated error handling (Weiß et al. 2015). In the context of this work it is assumed that an instance of a continuous process with an end event is reached as soon as the cyclic execution is interrupted by specific conditions and the system is transferred to a defined state. In Weiß et al. (2015) on the other hand, the rewinding of complete process instances is described. In the process models described later in this paper, only a limited part of the process is repeated cyclically and can therefore be regarded as continuous. The complete process instance also includes the tasks for starting up and shutting down the process. An intersection to be mentioned between both topics can be found upon further research in the consideration of data streams. As described in Beaulieu-Jones and Greene (2017), continuous analysis is an essential component in numerous scientific experiments. In this context, reproducibility is cited as a goal, or its improvement. Once again, it is evident, even in the processing of continuous data streams within scientific workflows, that the cyclical processing of incoming data (PLC), here in the form of batch processing of data, is utilized (Olston et al. 2011). Nevertheless, there exists a distinct difference between continuous processes in the industry, which require, even during system shutdown and departure from continuity, transitioning the system into a defined and, above all, secure state. In the continuous analysis of data streams, the question arises whether a workflow focused solely on data processing also requires transitioning the underlying system into a secure state. It is important to emphasize that the characteristics of the underlying process environment play a significant role in assessing the necessity of this step. The continuous operation of a reactor is managed differently than the continuous analysis of data streams. Approaches to the continuous processing of data streams in scientific workflows can partly be integrated into the consideration of the continuous components of processes in process engineering. However, these need to be expanded with additional conditions and measures.

Two elementary standards in the process industry are ISO Central Secretary (2010), the specification for diagrams for process industry, and ISO Central Secretary (2015), schemes for the chemical and petrochemical industry. The standard ISO Central Secretary (2015) describes how process plants in chemical and petrochemical industry and their elements are to be depicted in diagrams, while ISO Central Secretary (2010) provides the specification for a wider range of industrial processes. As stated in ISO Central Secretary (2010), diagrams following the introduced specifications shall be used not only for the construction of the process environment (process plant), but also during the complete operation of the system as means to represent the individual hardware elements. The differentiation into overview and function diagrams as described in ISO Central Secretary (2010) serves a similar purpose as the differentiation of a process model in BPMN into an overview process and multiple sub-processes. While overview diagrams give a general view of the complete process environment (e.g., complete plant or reactor including sensors and actuators) and point the user to more detailed sections, the functional diagrams give insight into the functionality of the respective elements. However, according to ISO Central Secretary (2010), overview diagrams can be depicted in form of network maps, block diagrams, process flow diagrams, and piping and instrumentation diagrams. This variety of different ways of presentation offers the possibility to individually depict and consider the specific aspects of a process system. However, these standards for chemical engineering focus on hardware description and do not describe the control logic that is necessary for execution of the process. This aspect is covered by programming languages specified in International Electrotechnical Commission (2013). Here, graphical notations such as function block diagrams, function charts and ladder diagrams, but also textual languages such as structured text and instruction list are defined. Despite the variety of these standards engineers still need to transform these standards into low-level equivalents for model checking purposes Ovatman et al. (2016). Issues such as the representation of time constraints and cyclic execution of process flows while examining the complete control system as a whole as well as maintenance of process models of higher complexity in a PLC are discussed. Since SFCs are well suited for modeling parallel processes, they have been widely used in recent years. Petri nets and timed automata in particular have become established for temporal processes (Ovatman et al. 2016).

In this work, control cycles were deliberately chosen as the basis for representing continuous processes as these are used for the description of control flows in continuous processes. Since previous scientific work has mostly focused on the design phase in the process life cycle, specifically with an emphasis on data, bridging the gap between business process management and chemical engineering can build a basis for new approaches in other process life cycle phases (e.g., configuration, enactment and evaluation) and also allows to deal with other perspectives (e.g., time and resources) in more detail. Modeling approaches and process-awareness as known from business process management enables to capture the behavior and hence to gain new insights based on process mining methods.

In terms of process engineering, processes are divided into two groups, i.e., batch and continuous processes. A characterizing description of continuous processes in comparison to batch processes is given in a Siemens manual.⁷ According to this manual, a continuous process is characterized by a "Continuous product flow" the main differences between continuous and batch processes can be found in properties such as process types, quantification of products, predictability, operating time, and level of operator interference. Continuous processes are characterized by a continuous mode of operation, whereby a specified state is always to be reached or maintained (Continuity).⁷ Operator intervention is rare but possible, and the degree of automation in such systems is high (Dietzsch et al. 2007). Since individual steps of the process are run through simultaneously during operation due to this continuity, it must also be possible to display or arrange them in parallel in a model (Parallelism), in a similar way as for the parallel execution of control loops (Ovatman et al. 2016). According to Tröster (2005) continuous systems are characterized by parameters which may take any value in a defined boundary. Further, Tröster (2005) conclude that the frequency, in which data access and control tasks are performed, determines a discontinuous behavior which needs to be counteracted by finding a fitting control strategy (Real-time). Due to hardware performance constraints truly continuous behavior may not be realized as physical sensors can only provide data in short time intervals. Therefore, characteristics of continuous processes include state queries and control tasks based on the state queries. Further, the timing of control processes in this context is of particular importance. Due to a high level of automation, elements must also be provided to allow the process to run or shut down in a controlled and safe manner in the event of exceptional circumstances such as emergency cases or maintenance (break conditions, exception handling) (Schmid 2015). Finally, understanding the process and, consequently, the comprehensibility of the presentation of the process (limited complexity) in question are the basis for checking and finding errors (Ovatman et al. 2016). Based on functional descriptions from technical literature (Schmid 2015; Dietzsch et al. 2007; Tröster 2005; Ovatman et al. 2016), a series of characteristics for representing continuous processes is proposed. From these elements, requirements for modeling continuous processes have been derived.

In addition to the extraction from technical literature, a literature study was carried out to underline the importance of the requirements. A detailed description of how the literature study has been conducted can be found in the supplementary material⁸ in Appendix E. A literature study has been conducted on publications from the last 20 years, searching for the keywords “automation industry continuous process” using the Scopus database. This search generated 336 of initial results. Search criteria are a publication date from 2002, written in English as well as the search terms had to appear in the title, in the abstract or in the keywords. The final selected results were examined to determine whether the requirements listed here were addressed in the work. For example, real-time processing is represented by the reference to time dependency of parameters and the emphasis on real-time data collection and control commands. Exception handling refers to the need to be able to react automatically to various unforeseen conditions without endangering people or equipment. Break conditions indicate that even in continuous processes, a start-up and shut-down phase must be observed or maintenance work must be carried out. Continuity was given if explicit reference was made to the continuous operation of the processes under normal conditions. Simplicity refers to the influence of operators and the optimal presentation of the processes. Parallelism is represented if the simultaneous execution of control tasks or the parallel planning of plant components was described in the publication. Table 1 displays the number of approaches that feature each of the six identified modeling requirements. The corresponding list of references along with details about the further selection process can be found in the supplementary material in Appendix E.

Each of the Requirements 1 – 6 can be found in scenarios in the process industry. The description of a scenario is illustrated based on the example of a temperature control system using a PI controller based on explanations from “The MathWorks® Support” documentation for version R2022a.⁹ The example is shown in Fig. 5 and was modeled with standard BPMN using Signavio.¹⁰ Basically, constructs to model Requirements 1–6 are available in standard BPMN, but they cannot be implemented explicitly. An example process is modeled in three different styles in order to demonstrate the differences using standard BPMN (see Fig. 5) and BPMN in combination with the suggested extensions (see Fig. 6). The third model version as implemented in the Cloud Process Execution (CPEE) engine¹¹ (see Fig. 7) as used as example in the expert interviews in Sect. 4. Figure 5 shows that cancel as well as measure and control tasks have been inserted, but they do not differ graphically. Besides reading the labels, there is no visible difference between the section for measuring tasks and the section for control parts. Using the suggested extensions, the measuring, control and cancel tasks are preceded by the appropriate and visually distinctive event. Further, instead of a sub-process with multiple exclusive gateways for a breakout from continuity that can happen at any time a new gateway is introduced to (1) explicitly mark the continuous part of the process, (2) automatically include cancel events with conditions to break out from continuity, (3) start every line for measure and control tasks with specified timer events, (4) automatically initiate parallel modeling of measure, control and cancel tasks, (5) enable modeling of “Clean-up” tasks after a cancel event has been triggered and (6) reduce multiple exclusive gateways and cancel end events. Hence, BPMN modeling extensions enabling the characteristics and clear representation of continuous processes are elaborated in Sect. 4. The end user should be enabled to model processes and generate executable code from them. This model-driven approach builds a bridge to the automation of processes in a similar way as described by Drave et al. (2022). A process is modeled using BPMN. Then, the execution code is developed based on this model (i.e., in a model-driven manner) with the help of a suitable execution semantics. The execution code then enables the automation of the process.

As described by Gläser and Laudel (2009), experts in the context of expert interviews are selected based on their knowledge of a specific subject that sets them apart from other colleagues. In this case, the decisive criteria for the selection of experts are their professional background as well as their practical experience in the context of the subject area. In this work, the modeling of continuous processes is considered the subject area. The group of interviewees should be composed of people with detailed knowledge of continuous processes and knowledge in process modeling. Due to the objective, it is also taken into account that people from a purely industrial environment may have never used BPMN, and people dealing with the modeling of business processes have never come into contact with industrial continuous processes. In order to obtain a comprehensive picture of the current status of the BPMN modeling extensions and their current potential, three groups were formed for the selection of experts, defined as follows:For the interviews 4 people per group were invited. The final set of twelve experts consists of national and international professionals from the industrial and research sector.

The interview guide was constructed following the instructions described in Bogner et al. (2014). A more detailed description of how the guide was compiled can be found in Appendix B¹⁴ of the supplementary material to this work. It was compiled based on the research questions listed in the introduction of this work and is divided into three parts: in the first part of the interviews, the experts are asked about key features as well as graphical features for the representation of continuous processes. In the section dealing with the evaluation of the BPMN modeling extensions with regard to usability and comprehensibility, among other things, examples of process models are shown to the experts, which they are to evaluate according to the following criteria: Comprehensibility, clarity, simplicity, logic and extensibility. In the third part of the interview, the experts are asked about challenges, weaknesses, and opportunities for improvement in the BPMN modeling extensions. These three parts of the interviews are not sharply separated from each other. The questions on the individual sections are partly intermixed in the interview guide. The interviews were conducted using two versions of the interview guide. Additional material to this work such as the interview guides, transcripts, and more detailed description to individual sections in this work can be found in the supplementary material.¹⁵

For the analysis of the interviews, the auditory recordings were transcribed. During the course of the interview with “Engineer” 3, there was an interruption resulting in a part of the responses to questions Q6 through Q10.6 not being recorded. The corresponding data is therefore missing in the following evaluation and classified in the graphical representation under “N/A” with the meaning “not available”. The transcription is done according to a set of rules that can be found in the supplementary material to this paper. Transcripts were sent to the appropriate experts for approval.

Qualitative content analysis is used to evaluate the transcripts. Our approach is based on the instructions by Kuckartz (2014) and its execution is similar to the procedure depicted in Hopf et al. (1995), which is also described in Kuckartz’s overview. These steps include the initial review of the transcripts and thus an initial categorization based on the material, classification of text sections according to the formed categories, preparation of tables for the summary of results, graphical representation as well as a discussion of the individual interviews, of the three groups, and as an overall comparison. The reason for choosing this method of analysis is the heterogeneity of the available data. This heterogeneity is partly due to the differences in the two interview guide versions, in which questions are partly formulated differently and thus generate divergent answers. Also, some of the expert’s answers are incomplete caused by the course of the interview and some technical failures (missing answers from “Engineer” 3 due to a lack of audio recording for some sections). The basic procedure in this work for the content analysis follows the explanations of Mayring (2015) as well as additional selectively applied instructions of Kuckartz (2014).

Due to the partly open questions and the pre-formulated questions in the interview guide, we apply the inductive category formation which falls under summary (Mayring 2015). In addition to the categories which can be implicitly derived from the questions of the interview guide, inductive category formation allows us to address unpredictable statements and to evaluate the information in the context of the interviews.

In this section, the results derived from the content analysis of the interviews (see methodology description in Sect. 6.3ff. above) are presented and discussed. In the following, the results are broken down into three parts as in the listing in Table 7, i.e., Part 1 presents general features of continuous processes with a focus on the graphical representation, Part 2 presents the evaluation of the comprehensibility and usability of the BPMN modeling extensions based on two process examples, and Part 3 presents the interpretation of the statements on challenges, weaknesses and recommendations for improvement of the BPMN modeling extensions. To give an overview of the knowledge gained, the results are shown in raw numbers in Table 8.

It should be emphasized that the obtained results give only a fraction of an insight into the opinions of the three defined groups of experts. Only 4 experts were interviewed for each group, with two experts each from the “Engineers” group also being asked different questions due to the two interview guide versions. In addition to the group size, influences originating from personal impressions and background of the experts must be taken into account. Effects such as the John Henry effect can influence the experts’ responses and bias the results (Saretsky 1972). The experts are aware that they were not the only ones to be interviewed in the context of this work. Furthermore, it can be assumed that due to the existence of standardized modeling methods in process engineering, the experts from the “Engineers” group responded with a more critical view. “Engineer” 2 stated that because it was a new presentation format, it was difficult to understand in a short amount of time what was depicted. Extensions of the existing standards may lead to an increased need for further training in this field, which can go hand in hand with increased effort and uncertainty in professional life. Another critical factor to consider here is the choice of specific process examples. While two thirds of the experts have at least moderate experience with industrial processes, the “Modelers” group is characterized by a lack of this knowledge and can therefore only provide limited feedback in this context. Generalizable results can only be expected if a larger number of experts is surveyed. Future evaluations with representatives of the described groups can be carried out with a larger number of participants but a smaller number of questions or topics in order not to strain the motivation of the participants and thus negatively influence the results.