Predictive Control Strategies for Automotive Engine Coldstart Emissions

In this study, a comprehensive investigation is carried out to study the effectiveness of model-based predictive control strategies to solve a formidable automotive control problem, that is, reducing the amount of cumulative hydrocarbon (HC) tailpipe emissions or HCcum over the first few minutes of an automotive engine operation which is known as the coldstart period. More than 80% of the total HC emissions for a typical driving cycle are generated during the coldstart period. There is a physical trade-off between increasing the exhaust gas temperature (Texh) and reducing engine-out hydrocarbon emission (HCraw-c), which are two key variables affecting the engine performance during the coldstart operation. The design of an effective coldstart controller is associated with lots of difficulties because the behavior of the engine in the coldstart period is highly transient, uncertain, and nonlinear, and also, the key factors are in confliction with each other. In the light of promising reports on the performance of model predictive controllers (MPCs), here, different variants of MPCs are taken into account to find out whether they can effectively cope with the difficulties associated with the coldstart problem for a given automotive engine. The major advantage of MPCs refers to their power to handle different constraints while trying to minimize an objective function to come up with optimal controlling signals. Other than the standard version of MPCs, in this work, some novel versions of such controllers are proposed, which are best suited for the considered control problem. The considered versions of MPCs are: nonlinear MPC (NMPC), preference-based model predictive controller (PBNMPC), and receding horizon sliding controller (RHSC). Also, a powerful classical optimal controller based on the Pontryagin’s minimum principle (PMP) is taken into account to ascertain the veracity of the considered predictive controlling methods. Through an exhaustive simulation, the efficacy of proposed predictive controlling techniques is demonstrated, and also, it is indicated how well such controllers can optimize the related objective function at the heart of coldstart control problem while handling a set of the operating constraints

Statistical Learning and Stochastic Process for Robust Predictive Control of Vehicle Suspension Systems

Predictive controllers play an important role in today's industry because of their capability of verifying optimum control signals for nonlinear systems in a real-time fashion. Due to their mathematical properties, such controllers are best suited for control problems with constraints. Also, these interesting controllers can be equipped with different types of optimization and learning modules. The main goal of this thesis is to explore the potential of predictive controllers for a challenging automotive problem, known as active vehicle suspension control. In this context, it is intended to explore both modeling and optimization modules using different statistical methodologies ranging from statistical learning to random process control. Among the variants of predictive controllers, learning-based model predictive controller (LBMPC) is becoming more and more interesting to the researchers of control society due to its structural flexibility and optimal performance. The current investigation will contribute to the improvement of LBMPC by adopting different statistical learning strategies and forecasting methods to improve the efficiency and robustness of learning performed in LBMPC. Also, advanced probabilistic tools such as reinforcement learning, absorbing state stochastic process, graphical modelling, and bootstrapping are used to quantify different sources of uncertainty which can affect the performance of the LBMPC when it is used for vehicle suspension control. Moreover, a comparative study is conducted using gradient-based as well as deterministic and stochastic direct search optimization algorithms for calculating the optimal control commands. By combining the well-established control and statistical theories, a novel variant of LBMPC is developed which not only affords stability and robustness, but also surpasses a wide range of conventional controllers for the vehicle suspension control problem. The findings of the current investigation can be interesting to the researchers of automotive industry (in particular those interested in automotive control), as several open issues regarding the potential of statistical tools for improving the performance of controllers for vehicle suspension problem are addressed

Pheochromocytoma-induced reverse tako-tsubo with rapid recovery of left ventricular function

Pheochromocytoma is a rare, catecholamine-secreting tumor of neuroendocrine cells. It has been documented to present atypically as myocardial ischemia, arrhythmias, or congestive heart failure. We present the case of a patient who had transient cardiomyopathy with hypokinesia of the basal portions of the left ventricle and hyperkinesia of the apex triggered by a pheochromocytoma crisis similar to that of tako-tsubo cardiomyopathy, but with an inverse left ventricular contractile pattern (‘inverted tako-tsubo’). (Cardiol J 2012; 19, 5: 527-531

Comparative Study on Essential Oils of Lavandula officinalis L. from Three Different Sites with Different Methods of Distillation

Lavandula angustifolia Mill. syn. Lavandula officinalis Chaix was commonly known as lavender is a species of the genus Lavandula from Lamiaceae family is among the top 10 pharmaceutical plant. Lavender species are grown worldwide primarily for their essential oils, which are used in the food processing, aromatherapy products, cosmetics and perfumes. The purpose of this study was to investigate the essential oils composition of lavender (Lavandula officinalis L.) cultivated in 3 provinces, Esfehan, Tehran, and Alburzprovincein Iran. This research examines it has been done on effects of different methods of distillation and habitat conditions on quantity and quality of oil of Lavandula officinalis flowering top plants cultivated in three regions were collected and after drying at room temperature in shadow. Esstential oils were extracted with three methods of distillation (water, steam and water and steam). Thirty compounds were identified in the essential oils, respectively. Components of essential oils from the Lavandula officinalis L. were determined using gas chromatography (GC) and Gas Chromatography- Mass Spectrometry (GC-MS). The important components in the Kashan area from Isfahan provincewere1,3,8-p- menthatriene (37.7 upto 39.8%), γ- terpinene (17.1 upto 19%), Linalyl formate (13.1 upto 15.08%), oil yield were 8.54 upto 10.03%, respectively. The important components in the Alburzprovince were ,3,8-p-menthatriene (31.7 upto 34.2%), γ- terpinene (24.2 upto 26.4%), Linalyl formate (11.8 upto 14%), oil yield were 5.5 upto 6.12%,  respectively. The important components in the Tehran province were 1,3,8-p- menthatriene (32.5 upto 34.1%), γ- terpinene (25 upto 29.8%), Linalyl formate (7.8 upto 9%), oil yield were 10.26 upto 12.13%, espectively

Synthesis and characterization of 7-nitrobenzo-2-oxa-1,3-diazole (NBD)-labeled fluorescent opioids

Alkylation of sarcosine with 4-chloro-nitrobenzo-2-oxa-1,3-diazole (NBD-chloride) furnished a fluorescent tag that was coupled with a tetrahydrothebaine derivative and [beta]-naltrexamine, respectively, to yield the fluorescent opioids 7[alpha]-(1R)-1-hydroxy-1-methyl-3-(4-hydroxyphenyl)-propyl]-6,14-endoethenotetrahydrothebaine NBD-sarcosinate (ASM-5-10) and N-cyclopropylmethyl-3-hydroxy-14[beta]-hydroxy-6[beta]-(NBD sarcosinyl)-amino-epoxymorphinan (ASM-5-67). The fluorescence intensity of the novel opioids allowed their detection at subnanomolar concentrations, and was dependent on the polarity of the solvent. Maximum quantum yield was obtained in ethyl acetate and ethanol, and minimal fluorescence in heptane and water. Compounds ASM-5-10 and ASM-5-67 displaced the opioid receptor binding of [3H]Tyr--Ala-Gly-(Me)Phe-Gly-ol in monkey brain membranes with IC50 values of 8.4 and 1.5nM, respectively. Whereas ASM-5-67 bound to [mu], [delta], and [kappa] receptors with comparable affinities, ASM-5-10 was [mu]-selective, with selectivity indices (ratio of respective IC50 values) of 0.04 for both [mu]/[delta] and [mu]/[kappa]. The sodium response ratio in binding revealed a pronounced agonist property of ASM-5-10. Both opioids were lipophilic, with octanol-water partition coefficients (log Papp) of 2.8 (ASM-5-10) and 1.0 (ASM-5-67). ASM-5-10 exhibited particularly strong membrane retention that was not reversible by four washes. Their favorable characteristics in fluorescence, receptor binding, and membrane interaction make these newly developed ligands useful molecular probes to study opioid receptor mechanisms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30242/1/0000637.pd

Proposing an agile strategy for a steel industry supply chain through the integration of balance scorecard and Interpretive Structural Modeling

The internal and external environment of all organizations challenges them adapt to the best practices and reconsider their relationship throughout the supply chain. In this paper, the authors have tried to propose an agile strategy for the supply chain of a steel company, which ranks 3rd in Iran in Gross Sale with 16,000 employees, in order to respond quickly to ever-changing needs. To do this, through the literature review the framework of balanced scorecard was utilized to structure agility factors in the supply chain of the steel industry. Then the experts were interviewed to reconcile on the factors identified. Utilizing 24 questionnaires by the use of Interpretive Structural Modeling framework, the relationship and sequence of factors were obtained from experts. The final model developed in the paper presents the agility factors for the steel industry supply chain. Also, these factors are grouped within the four perspectives of the BSC to better enhance the results and pursue action. The ISM method identifies the priority of each factor which provides a better understanding of the underlying relationship of the factors for the managers to implement the strategies more reliably. The proposed model for strategy formulation can be utilized in strategy formulation problems over various types of supply chain