Modelling of the auto-ignition angle in diesel HCCI engines through D-optimal design

The HCCI (Homogeneous Charge Compression Ignition) process is one of the most promising combustion processes developed to reduce pollutant emissions from automotive vehicles. However, there are practical difficulties concerning the control of the onset of ignition, and thus the availability of simple models which allows to simulate the auto-ignition phenomena may be very interesting for the development of new HCCI engines. In this work, the onset of ignition in a HCCI engine and the auto-ignition angle were modelled (OAM and AAM respectively) through experimental plans based on the D-optimal criterion. The experimental values were obtained by using the chemical kinetic code CHEMKIN together with an appropriate diesel fuel surrogate. The models developed have an acceptable goodness-of-fit and predictive capability (differences lower than 3 CAD were obtained between modelled and real auto-ignition angles for all the cases). The relative fuel/oxidant ratio and the intake temperature were the most significant engine parameters affecting the onset of auto-ignition, while the intake temperature and pressure appear as the most important parameters determining the auto-ignition angle. These models could be used by the Engine Control Unit (ECU) as an on-board diagnostic technique to control the HCCI combustion in real time. The optimal engine parameters for five specific operating conditions (chosen to cover the most common light duty diesel vehicles operating modes) were also calculated by using the above mentioned models (OAM and AAM) and by solving two non-linear optimization problems. To achieve optimization, a desirability function was defined. The optimization methodology proposed can be used to obtain the optimum engine parameters, which are used by the ECU, matching different vehicle requirements.     

Influence of Different Factors on Firmness and Color Evolution During the Storage of Persimmon cv. 'Rojo Brillante'

‘Rojo Brillante’ persimmons were harvested in 2 different dates, early and late, and then submitted to 1‐methylcyclopropene (1‐MCP) treatment (500 nL/L) before stored at 1 or 15 °C up to 50 or 30 d, respectively. The influence of harvest date, 1‐MCP treatment, orchard, storage time, and temperature on firmness loss and color evolution during storage of ‘Rojo Brillante’ persimmon was studied. Statistical models that allowed the prediction of these 2 quality parameters during the storage, as well as the modeling of the behavior of both of them, were also evaluated. The softening, as a consequence of chilling sensitiveness when stored at 1 °C, was more accentuated in the early harvested fruit. In storage at 15 °C, also, the firmness loss was faster in fruit harvested earlier. 1‐MCP treatment drastically reduced chilling injury (CI) symptoms at 1 °C and retarded firmness loss at 15 °C, both of these effects being dependent on harvest date. The model presented to study the firmness and color relationship offers an important tool to predict firmness from fruit color measurements.     

Some Modifications of the Classical Pre‐control Technique

Pre‐control is a simple technique for the initial evaluations of the capability of a process. It can be seen as a tool to get the set‐up approval or fulfilment of the specifications of a production process. As the resultant information of pre‐control should be used to adjust the process, it can be understood as a form of feedback controller. It has sometimes been considered as an alternative to statistical control charts for monitoring processes, although these tools differ in a number of ways. In this work, we propose some new alternatives to the classical pre‐control, particularly in its initial phase that aim to qualify the process, that is, to certify that it is capable. We present a comparative analysis of the power of the different alternatives. Copyright © 2003 John Wiley & Sons, Ltd.     

Statistical differentiation of wines of different geographic origin and aged in barrel according to some volatile components and ethylphenols

The purpose of this paper was to study the effect of wine composition and geographical origin on the accumulation of volatile compounds in barrel-aged wines. Therefore, 267 wines belonging to different origin appellations were analysed. In order to analyse the data, a multivariate statistical technique was applied, that can be quite useful in creating an overall view of the problem in which multiple variables are analysed. Statistical analysis gave four significant factors, accounting for 84.12% of the variance. These factors summarise the information of a group of variables closely correlated to each other. Factor 1 grouped together oak lactones and eugenol, so it was associated with oak barrel type and with toasting of barrels. Factor 2 was associated with ethylphenols, related to wine quality diminution. Factor 3 grouped together guaiacol, furfural, and 5-methylfurfural, and was thus associated with wood thermal degradation; factor 4 was associated with the cis/trans ratio, related to oak origin. Whereas geographical origin of wines affected the accumulation of volatile compounds, the cis/trans ratio was only affected by oak barrel type. Among the tested parameters, alcoholic degree was the enological parameter that had the greatest effect on the accumulation of volatile compounds in wines. Oak lactones were the compounds that were present in all wines, so they could be used as oak ageing indicators.     

Combined double sampling and variable sampling interval X chart

In recent years several studies have shown that the X chart with variable sampling intervals (VSI), the X chart with variable size (VSS), the X chart with variable sample size and sampling intervals (VSSI) and the X chart with variable parameters (VP) detect both small and moderate shifts in the process mean more quickly than the traditional Shewhart X chart. Double sampling is the counterpart to double sampling plans. A combined double sampling variable sampling interval (DSVSI) X chart is studied in this paper. It is compared with the aforementioned charts and with the CUSUM and EWMA charts. In all cases, the DSVSI X chart is quicker at detecting small and moderate shifts in the process mean. An example is provided.