Isooctane is a valuable octane enhancer for gasoline and the primary component of aviation gasoline, also known as Avgas, because of its high antiknock quality. Conventional industrial processes for isooctane production involve the steps of dimerization of isobutene, dimer separation, and hydrogenation. The efficacy of catalytic distillation (CD) and its merits, in terms of energy savings and reduction of greenhouse gas emissions, for the production of isooctane are quantitatively presented. The feed considered for the isooctane production is composed of isobutene (C4) and inerts (isopentane) produced in refineries as byproducts of steam cracking of naphtha and light gas oil. Process flow sheets for the two routes for the production of isooctane, with and without CD, are modeled. The conventional industrial flow sheet composed of a dimerization reactor, distillation column, and a hydrogenation reactor (configuration A), is simulated using Aspen Plus. The intensified process flow sheet comprising a CD column for the dimerization, hydrogenation, and separation (configuration B) is modeled using gPROMS. A validated, nonequilibrium, three-phase model is developed in a gPROMS environment and is used to quantify the energy savings and reduction of carbon dioxide emissions achieved using a CD column for the intensified process. Results demonstrate CD to be a promising candidate to replicate the conversions and product purity obtained in the conventional process while resulting in significant energy savings, more efficient utilization of isobutene feed, and reduced carbon dioxide emissions.

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A frequent problem experienced throughout industry is that of missing or poor quality data in data historians. This can have various causes, such as field instrument failures, loss of communication, or even issues with the setup of the historian itself. The end result is that data required to perform analyses needed to improve facility operations may be unavailable. This generally incurs delays, as the data analyst must manually "clean up"the data before using it, or could even result in erroneous conclusions if the data is used as is without any corrections. In this paper, a novel multivariate statistical method is proposed to detect incorrect data values and reconstruct corrected values to be stored in the historian. This method works on streaming data, and thus makes its corrections continuously in near real-time. The method has been successfully tested in a laboratory setting using real operating data from a Chevron facility. Chevron plans to test the data error detection and reconstruction method in the field in the near future. Use of this method will ensure that good quality data for needed analyses is available in the data historian, and will save analyst time as well.

Improvements in sampling methods and sensor technology have resulted in the creation of vast amounts of process data that can be analyzed to improve the operation of chemical facilities. Automated real-time data analysis methods sort through incoming data immediately, aiding in real-time decision making and preparing the data for further analysis. In this study, the application of online wavelet transforms (OWT) to several aspects of real-time data analysis was considered, namely, fault detection, data cleansing and data compression. In the first application, the OWT was used to build a model and detect faults in an uncorrelated tag (variable) which cannot use traditional data driven modelling that relies on underlying relationships between variables. The OWT was successfully able to detect and identify various common sensor faults including missing and frozen values, linear drift and spikes. Recursive least squares (RLS) was then used to predict replacement values, and was a significant improvement on current standards, particularly with highly variable data. The new algorithm (OWT and RLS together) also updates statistics with each new value, so decision making is accurate but does not require repeated calculations on the whole data set. This application directly affects real-time decision making as detected faults can be addressed immediately, and any predictive models built from the data will be representative of real operation. The OWT is also used to create a novel real-time data compression algorithm to economically store large quantities of data. The industry standard swinging door algorithm requires extensive manual input, in addition to other drawbacks. The new method is an online filter that is completely automated, with calculations unique to each tag. This new algorithm showed much higher compression ratios with less loss of information, and was clearly superior to the old method.