Système d'arrêt-démarrage automatique

Le système d'arrêt-démarrage automatique (aussi désigné par le faux anglicisme stop and start par certains constructeurs) est un dispositif automobile d'interruption du moteur à combustion permettant d'économiser le carburant et de réduire la pollution associée, essentiellement en ville et dans les embouteillages. Historique Le moteur/générateur n'est pas une nouveauté, car les automobiles luxueuses des années 1930 étaient souvent équipées de « dynastar », une grosse dynamo/démarreur, généralement montée directement en bout de vilebrequin et dont l'avantage principal était de permettre un démarrage souple et silencieux du moteur. Les émissions de monoxyde de carbone ainsi que le premier choc pétrolier ont poussé les fabricants d'automobiles vers des technologies visant à réduire la consommation de carburant. En 1974 déjà, Toyota commercialisait sa Toyota Crown à six cylindres avec un interrupteur démarrage-arrêt automatique dans le but d'économiser 10 % d'essence dans le

Mutually Coupled Time-to-Digital Converters (TDCs) for Direct Time-of-Flight (dTOF) Image Sensors

Edoardo Charbon, Preethi Padmanabhan, Augusto Ronchini Ximenes

Direct time-of-flight (dTOF) image sensors require accurate and robust timing references for precise depth calculation. On-chip timing references are well-known and understood, but for imaging systems where several thousands of pixels require seamless references, area and power consumption limit the use of more traditional synthesizers, such as phase/delay-locked loops (PLLs/DLLs). Other methods, such as relative timing measurement (start/stop), require constant foreground calibration, which is not feasible for outdoor applications, where conditions of temperature, background illumination, etc. can change drastically and frequently. In this paper, a scalable reference generation and synchronization is provided, using minimum resources of area and power, while being robust to mismatches. The suitability of this approach is demonstrated through the design of anrange, 125 ps least significant bit (LSB), and 14-bit resolution. Phase and frequency locking among the ROs is achieved, while providing 18 dB phase noise improvement over an equivalent individual oscillator. The integrated root mean square (RMS) jitter is less than 9 ps, the instantaneous frequency variation is less than 0.11%, differential nonlinearity (DNL) is less than 2 LSB, and integral nonlinearity (INL) is less than 3 LSB.

2018

Automatic detection of reaction start/endpoints in chemical and biotechnological reaction systems

Michael Amrhein, Simon Crelier

A classification methodology for the automatic detection of start- and endpoints of chemical and biotechnological reaction systems from spectral reaction data is proposed. In the calibration phase, several batch experiments must be conducted covering the expected operational variability (e.g., initial concentrations, dosage amount and time). The start/endpoint of the reaction of interest (e.g. start of a side reaction, stop of the main reaction) is determined by using Evolving Factor Analysis (EFA) and cross-checked with common process variables (temperature, mass, heat flow) or chromatographic measurements if available. A Partial Least-Squares Discriminant Analysis (PLS-DA) model is built on the calibration batches and then used for on-line detection of the start/stop of the reaction of interest based on spectral data from a new batch. The methodology is illustrated for (i) simulated spectra of a fed-batch reactor exhibiting two consecutive reactions with a limiting initial reactant for the first reaction, and (ii) measured infrared spectra of an aldol reaction exhibiting a side reaction. A PLS-DA model is built to on-line detect the stop of the main reaction. The effect of data pre-treatment methods and the choice of the number of latent variables on various classification performance indices (efficiency, false positive rate and false negative rate) are evaluated. For the simulated data, the best model is obtained with four latent variables and mean-centered data. The classification efficiency for a validation set of five experiments is 99.0% and the delay for the endpoint detection is about 1 min which corresponds to the sampling time. For the measured data, the best model is obtained with five latent variables and pre-treatment using standard normal variate. The classification efficiency for a validation set of two experiments is 95.9% and the delay for the endpoint detection is about 8 min for typical batch duration of 250 min.

2007

Automatic detection of reaction start/endpoints in chemical and biotechnological reaction systems

Michael Amrhein, Simon Crelier

2007