# Low Temperature Combustion Modeling and Predictive Control of Marine Engines
## 4 Discussion  
The results indicate a feasible performance of the developed system. The nonlinear RCCI COM achieved the simulation time in order of milliseconds, which was faster than the original target. Additionally, it achieved an accuracy within the target 5% limit compared to the UVATZ model. The model-based control system provided feasible results in both nominal and transient conditions with low tracking errors within the target 5% limit. In certain operating conditions, the COM did not achieve the desired absolute accuracy despite a good trendwise accuracy. This might be due to the utilization of a single Wiebe function for estimating CHR, HRR and \(\theta_{50}\). \(\theta_{50}\) was well estimated as shown in results, but \(Q_{c}\) and \(dQ_{c}\) do not achieve the same level of accuracy as previously discussed in Section 3.2. One contributing factor to this discrepancy, in addition to the simplicity of the Wiebe function, is the value of the efficiency factor. The UVATZ model calculates the combustion efficiency for each cycle, but in COM the efficiency is set to a fixed value. The UVATZ model solves the detailed chemical kinetics of its zones to maintain its predictive nature. The in-cylinder fuel and thermal stratification govern the order of zonal ignitions. For this reason the HRR is a superposition of heat released in each zone causing the \"spiky\" signal, which does not correspond to a real-world experimental results. However, the integration of the HRR of UVATZ results in an accurate CHR. Nevertheless, it can be assumed that single Wiebe is not able to fully capture HRR of the UVATZ, and since HRR is used in the in-cylinder pressure model, its affect can be seen as low estimation accuracy of \(p_{\text{max}}\) and subsequently in-cylinder temperature and \(\text{IMEP}_{\text{gross}}\). It is worth mentioning that in this work the Wiebe function was primarily optimized for estimating \(\theta_{50}\). Therefore, low accuracy in the CHR and HRR trends were expected. Despite these limitations, the controller is able to achieve feasible accuracy for \(\text{IMEP}_{\text{gross}}\) as well for all the test cases. The developed model-based control system shall be further improved and expanded based on the following propositions:  
* Expanding the operational range of the nonlinear RCCI COM. This means further optimization of the model. * Multiple Wiebe could improve the CHR and HRR estimations and subsequently improve the in-cylinder pressure and IMEP estimations without increasing the computation time. * Expanding the COM with physics-based emission models. This provides the possibility to analyze the correlation between the main combustion performance indicators, especially \(p_{\text{max}}\), \(\text{PRR}_{\text{max}}\) and \(T_{cyl}\), and \(\text{NO}_{x}\), \(\text{CO}_{x}\) and UHC. * Incorporating \(p_{\text{max}}\), \(\text{PRR}_{\text{max}}\) and \(\text{NO}_{x}\) in the control design as constraints to maintain desired performance and emission levels. * Further investigation of the controller's performance in the presence of disturbances. * Applying multi-injection functionality to further improve the efficiency of the combustion.