# _Separtial system innovation_
## 9 Results
### Research Questions
Next, to better understand how AI is exploited in the common practice of process engineering and to answer RQ.2, papers have been classified (as first employing keywords, then with a final refinement and reclassification after examining the contents, which often did not confirm the fitness of chosen keywords) according to one between six uses of it: Control, Design, Fault Diagnosis, Modeling, Optimization and Prediction. Figure 7 and Table 2 illustrate the distribution and counting of significant papers across various application categories, respectively. The most frequent application is fault diagnosis, which is a ubiquitous issue across all engineering fields. This prevalence allows leveraging existing experiences and methodologies with minimal translational effort. Furthermore, fault diagnosis is a common practice, enabling a straightforward comparison between innovative AI-based approaches and well-established techniques in the same cases. This cross-validation greatly benefits from the pre-existing process engineering know-how already available to researchers and practitioners. The second most frequent applications are modeling and prediction. These categories are closely related, as predictive modeling and the extraction of synthetic descriptions from datasets are typical uses of AI techniques, representing two sides of the same coin. Data collection is necessary for the monitoring and control of process plants, and in some cases, it is mandated by regulations. As a result, datasets are generally readily available and can be easily obtained from the regular operation of process plants or can be specifically collected to establish new operational practices. In this context, AI enables better exploitation of collected data and, in some cases, allows real-time analysis compared to traditional computational methods. The application of AI in design spans a broad range of possibilities, such as the exploration of design parameter space or its structure. The high frequency of studies focused on modeling and prediction can be interpreted as an indicator of ongoing efforts to shift toward an AI-based approach in design. This exploratory phase signals a gradual transition toward more systematic AI integration within the design process, reflecting an active area of research and application. The limited number of papers in these areas may indicate that optimization and control often require more rigorous validation before implementation in industrial settings. These domains demand not only robust AI models but also stringent testing to ensure that AI-driven solutions can operate safely and reliably within the constraints of existing systems. As the AI and process engineering communities continue to collaborate, the exploration of AI applications in optimization and control is expected to grow, particularly as the demand for efficiency, sustainability, and agility in process industries continues to rise. Also, this finding seems to corroborate the preliminary result, moving the balance toward a lower level of maturity, as the dominant category is rather connected to engineering in general and off-line, generic activities instead of contributing in-the-loop to process operations, nor especially impact on field-specific aspects. \begin{table}
\begin{tabular}{l r} \hline \hline
**Category** & **Count** \\ \hline Fault Diagnosis & 30 \\ \hline Prediction & 29 \\ \hline Modelling & 21 \\ \hline Optimisation & 9 \\ \hline Design & 6 \\ \hline Control & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RQ.2 Categories and their Counts. Figure 7: Stacked Bar Chart: the bars represent the counting based on RQ.1, while the stacks represent the counting concerning RQ.2. Concerning the RQ.3 and RQ.3.1 research questions, another classification of papers (including all three categories) may be done following the family of techniques: Machine Learning or Deep Learning.