Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 2: modelling and optimization

This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.

Understanding Bile Micelle Chiral Interactions

Bile salts are known to aggregate into micelles in biological systems; however, the fundamental structure and dynamics of bile molecule micelle formation are poorly understood. Previous studies have established that the bile salt cholate is capable of performing chirally selective micellar electrokinetic capillary chromatography (MEKC) separations of model racemic binaphthyl compounds 1,1¿-binaphthyl-2,2¿-diyl hydrogen phosphate (R,S-BNDHP) and 1,1¿-bi-2-naphthol (R,S-BN). Nuclear magnetic resonance (NMR) has been established as a complementary technique for understanding chiral selectivity and micelle formation events based on changes in proton chemical shifts of the probe molecules BNDHP and BN as well as of cholate. This work investigated the effects of the probe molecule, the alkali cation identity and temperature on cholate micelle aggregation and MEKC separations of R,S-BN and R,S-BNDHP. The probe molecule was found to mediate micelle formation by MEKC and proton NMR. A low (0.1 mM) concentration of probe was found to have minimal effects on micellization events detected by proton NMR while higher probe concentration (2.5 mM) was found to mediate micellization causing micellization events to occur at lower cholate concentrations. This work also investigated the effects of alkali counterion on chiral separation. Generally, counterions with larger crystal cationic radius were found to cause greater chiral separation power. NMR data suggest that protons near the surface of the cholate micelle are most sensitive to the cation identity, suggesting a model of improved separation based on the cation sterically inhibiting binding of one isomer. Finally, the effect of temperature on MEKC separation was investigated. Separation power of R,S-BN and R,S-BNDHP appeared to increase linearly with temperature for 22.0 mM to 50.0 mM pH 12.0 cholate. In total, these results indicate that cholate aggregation is dependent on multiple conditions. Understanding the roles that these factors play in influencing cholate micellization can inform better separation in MEKC.