Although steam cracking is a mature technology, mitigation of coke formation remains one of the main challenges in the petrochemical industry. To increase the olefin output of existing plants, coil materials that can withstand higher temperatures are desired. This work reviews material technologies that were developed and tested in the past three decades to minimize the rate of coke deposition and extend the furnace run length. The material not only determines the mechanical properties of the coil but also affects the coking rate substantially. In some cases, differences in coking rates by more than a factor 10 have been observed. SiC materials could be operated at significantly higher temperatures, and this leads to higher olefin selectivity if one includes acetylene hydrogenation; however, the mechanical joints make it currently impossible to take advantage of their superior temperature resistance. On the industrial scale, operational improvements have been reported with advanced reactor surface technologies such as high-performance alloys and coatings during the past decade. Catalytic coatings go a step further than barrier coatings by actively removing coke that is deposited on the coils. Another trend is to add aluminum to the coil material, which forms a protective aluminum oxide layer on the reactor wall during operation and results in reduced carburization. To optimize the coking mitigation capabilities of the coils, the state-of-the-art materials and/or coatings should be combined with 3D reactor technologies, which is not always possible for all materials because of the advanced machining that is needed.

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Stochastic terms are included in fundamental dynamic models of chemical processes to account for disturbances, input uncertainties and model mismatch. The resulting equations are called stochastic differential equations (SDEs). An approximate expectation maximisation (AEM) algorithm using B-splines is developed for estimating parameters in SDE models when the magnitude of the disturbances and model mismatch is unknown. The AEM method is evaluated using a two-state nonlinear continuous stirred tank reactor (CSTR) model. [Varziri et al., Can. J. Chem. Eng. 2008; 86: 828]). For the CSTR examples studied, the AEM algorithm provides more accurate estimates of model parameters, unknown initial conditions and disturbance intensities. SDE models and associated parameter estimates obtained using AEM will be helpful to engineers who subsequently implement on-line state estimation and process monitoring schemes because the two types of uncertainties that are considered (i.e. measurement noise and stochastic process disturbances) are consistent with the error structure used in extended Kalman filters.

The development of a relatively simple mechanistic model for an industrial ethylene cracking furnace is described, including the estimation of selected model parameters to improve model predictions. Energy balance equations are developed to account for radiative, conductive, and convective heat transfer in the radiant section, and for convection and conduction in the ultra-selective heat exchanger (USX) and in the transfer line exchanger (TLE). Kinetic schemes by Ranjan et al. and Sundaram and Froment are used to model the cracking reactions. [1,2] The heat transfer model is combined with mass and momentum balances to model gas composition, pressure, and temperature changes as a function of position along the reactor tubes. Initial values and uncertainty ranges are assigned to 44 model parameters based on information in the literature and our industrial sponsor. A sensitivity-based technique and a mean-squared-error (MSE) criterion are used to select the appropriate subset of 22 parameters for tuning. Parameters are estimated and model predictions are validated using industrial data. Model predictions provide a good match to data that were not used for estimation.