## 5 Conclusions  
In this work, we aim to design and showcase an adaptive process scheduling mechanism that incorporates predictive maintenance results as an input to take autonomous decisions that regulate an industrial process to minimize downtimes while keeping its operation safe and efficient. Our goal is to show that such a coupled autonomous planning \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**System**} & **PM-Based Adaptive** & **PM-Based Adaptive** & **Rule-Based** \\  & **Control (\(interval=0.5\))** & **Control (\(interval=13\))** & **Preventive Control** \\ \hline Efficiency(\%) & 74.657 & 67.008 & 66.566 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The average efficiency values of our PM-based adaptive control with \(interval=0.5\) r/min, 13 r/min and the rule-based system. mechanism is needed to complement a PM mechanism, which alone would not provide a significant improvement in the efficiency of the operation when there are no relevant decisions taken accordingly. For this purpose, we propose a pipeline to incorporate PM analysis into conventional industrial process scheduling, complementing maintenance planning approaches based on prognostics (e.g., prognostic health management, i.e., PHM, solutions). In our closed-loop system, each PM notification triggers an error analysis based on a PID error calculation, to find the system deviation from the nominal operation calculated by a twin model in real-time. The rest incorporates this deviation in scheduling an optimal operation mode. We state that our pipeline is agnostic to the scheduling algorithm selected. In our application, we select GA as it is one of the mostly used scheduling algorithm in industrial operations. The fitness function takes into account the calculated error and jointly optimizes on the operation speed (i.e., less or no downtime towards keeping the requested operation) and on the power consumed by the system (i.e., to compensate extra disturbances on the system for protection). We leave the optimization criteria optional to system users, that is, a user can select a protective mode (more weight on preserving power and the machine) or a performer mode (more weight on keeping an initially requested operation). In general, the larger the deviation from an expected operation, the more protective the system gets. In order to show the impact and the necessity of incorporating PM analysis into an automated adaptive process scheduling, we compared our final system with a rule/condition-based decision mechanism also relying on the same PM analysis as a baseline. That is, after every PM notification a rule-based decision system is triggered, replacing our GA-based adaptive scheduling, that schedules for a maintenance intervention and induces drastic changes on the system for protection. This experiment showed that without such integration, a PM algorithm with a fail-safe, rule-based mechanism would almost perform with the same efficiency of a complete preventive approach. This supports the necessity of complementing PM algorithms with such autonomous adaptive scheduling mechanisms and shows that our approach is capable of doing it. Finally, we compared the performance of our PM-based adaptive scheduling with a conventional rule-based preventive mechanism and showed that it significantly improves the efficiency (by 12.15%) and decreases downtime through adaptive recovery from transient abnormal conditions. During run-time, we use the twin model to calculate the nominal values for the requested operation. If developing a twin model is not applicable, which might be the case for more complex industrial processes, the nominal conditions can also be obtained from the fingerprint of the system. In that case, the fingerprint can only provide expected lower and upper bounds for the requested operation (see Figure 6). This, however, leads to less accurate error calculations, compared to the case with a twin model that provides precise nominal values.