Artificial intelligence in steam cracking modeling : a deep learning algorithm for detailed effluent prediction

Chemical processes can benefit tremendously from fast and accurate effluent composition prediction for plant design, control, and optimization. The Industry 4.0 revolution claims that by introducing machine learning into these fields, substantial economic and environmental gains can be achieved. The bottleneck for high-frequency optimization and process control is often the time necessary to perform the required detailed analyses of, for example, feed and product. To resolve these issues, a framework of four deep learning artificial neural networks (DL ANNs) has been developed for the largest chemicals production process-steam cracking. The proposed methodology allows both a detailed characterization of a naphtha feedstock and a detailed composition of the steam cracker effluent to be determined, based on a limited number of commercial naphtha indices and rapidly accessible process characteristics. The detailed characterization of a naphtha is predicted from three points on the boiling curve and paraffins, iso-paraffins, olefins, naphthenes, and aronatics (PIONA) characterization. If unavailable, the boiling points are also estimated. Even with estimated boiling points, the developed DL ANN outperforms several established methods such as maximization of Shannon entropy and traditional ANNs. For feedstock reconstruction, a mean absolute error (MAE) of 0.3 wt% is achieved on the test set, while the MAE of the effluent prediction is 0.1 wt%. When combining all networks-using the output of the previous as input to the next-the effluent MAE increases to 0.19 wt%. In addition to the high accuracy of the networks, a major benefit is the negligible computational cost required to obtain the predictions. On a standard Intel i7 processor, predictions are made in the order of milliseconds. Commercial software such as COILSIM1D performs slightly better in terms of accuracy, but the required central processing unit time per reaction is in the order of seconds. This tremendous speed-up and minimal accuracy loss make the presented framework highly suitable for the continuous monitoring of difficult-to-access process parameters and for the envisioned, high-frequency real-time optimization (RTO) strategy or process control. Nevertheless, the lack of a fundamental basis implies that fundamental understanding is almost completely lost, which is not always well-accepted by the engineering community. In addition, the performance of the developed networks drops significantly for naphthas that are highly dissimilar to those in the training set. (C) 2019 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company

Alumina-based coating for coke reduction in steam crackers

Alumina-based coatings have been claimed as being an advantageous modification in industrial ethylene furnaces. In this work, on-line experimentally measured coking rates of a commercial coating (CoatAlloy (TM)) have pointed out its superiority compared to an uncoated reference material in an electrobalance set-up. Additionally, the effects of presulfiding with 500 ppmw DMDS per H2O, continuous addition of 41 ppmw S per HC of DMDS, and a combination thereof were evaluated during ethane steam cracking under industrially relevant conditions (T-gasphase = 1173 K, P-tot = 0.1 MPa, X-C2H6 = 70%, dilution delta = 0.33 kg(H2O)/kg(HC)). The examined samples were further evaluated using online thermogravimetry, scanning electron microscopy and energy diffractive X-ray for surface and cross-section analysis together with X-ray photoelectron spectroscopy and wavelength-dispersive X-ray spectroscopy for surface analysis. The passivating coating illustrated a better performance than the reference Ni-Cr Fe-base alloy after application of an improved pretreatment, followed by piddling changes on the product distribution. Presulfiding of the coating affected negatively the observed coking rates in comparison with the reference alloy, so alternative presulfiding and sulfur addition strategies are recommended when using this barrier coating

Analytical Py-GC/MS of genetically modified poplar for the increased production of bio-aromatics

Genetic engineering is a powerful tool to steer bio-oil composition towards the production of speciality chemicals such as guaiacols, syringols, phenols, and vanillin through well-defined biomass feedstocks. Our previous work demonstrated the effects of lignin biosynthesis gene modification on the pyrolysis vapour compositions obtained from wood derived from greenhouse-grown poplars. In this study, field-grown poplars downregulated in the genes encoding CINNAMYL ALCOHOL DEHYDROGENASE (CAD), CAFFEIC ACID O-METHYLTRANSFERASE (COMT) and CAFFEOYL-CoA O-METHYLTRANSFERASE (CCoAOMT), and their corresponding wild type were pyrolysed in a Py-GC/MS. This work aims at capturing the effects of downregulation of the three enzymes on bio-oil composition using principal component analysis (PCA). 3,5-methoxytoluene, vanillin, coniferyl alcohol, 4-vinyl guaiacol, syringol, syringaldehyde, and guaiacol are the determining factors in the PCA analysis that are the substantially affected by COMT, CAD and CCoAOMT enzyme downregulation. COMT and CAD downregulated transgenic lines proved to be statistically different from the wild type because of a substantial difference in S and G lignin units. The sCAD line lead to a significant drop (nearly 51%) in S-lignin derived compounds, while CCoAOMT downregulation affected the least (7-11%). Further, removal of extractives via pretreatment enhanced the statistical differences among the CAD transgenic lines and its wild type. On the other hand, COMT downregulation caused 2-fold reduction in S-derived compounds compared to G-derived compounds. This study manifests the applicability of PCA analysis in tracking the biological changes in biomass (poplar in this case) and their effects on pyrolysis-oil compositions

Steam cracking coke properties and their influence on furnace run length predictions : experimental and modeling study

Although coke formation is one of the most important problems that the steam cracking process still faces, the properties of the formed coke have hardly been studied over the past decade. For some of them, orders of magnitude differences have been reported. Therefore, state-of-the-art measurement techniques have been used on industrially relevant samples obtained at laboratory and industrial scale for ethane and naphtha cracking. The feedstock type does play an important role, reflected in, for example, factor 2 differences in BET surface areas for ethane and naphtha coke samples. Molar C/H ratios from 26 up to 59 are obtained by CHNS/O analysis, depending on the age of the coke and the proximity of the metal coil. The measured thermal conductivity values for ethane and naphtha coke, that is, 9.6 and 8.7 W m(-1) K-1 at 1273 K, respectively, are approximately twice and three times the values reported by Dente and co-workers. A sensitivity analysis on the influence of these properties on the predicted run length of an industrial furnace gives huge differences, that is, from 2 weeks up to 212 days for naphtha furnace operation and 20 up to 227 days for ethane operation. This clearly indicates the importance and the need for more accurate assessment of coke properties to develop more predictive models for run length simulations

Impact of initial surface roughness and aging on coke formation during Ethane steam cracking

Alloy composition and morphology of the inner wall of steam cracking reactors are well-known key factors that affect their coking tendency. The effect of surface roughness on the coking tendency remains uncharted to date and has been studied here for a 35/25 Ni/Cr wt % alloy in a quartz jet stirred reactor equipped with an electro-balance under coil outlet industrially relevant ethane steam cracking conditions: Tgas phase = 1173 K, Ptot = 0.1 MPa, and XC2H6 = 70%. Up to 6 times higher initial coking rates have been observed during cyclic aging in an Rα surface roughness range of 0.15−7 μm, and cyclic aging proved to have an effect mainly on the catalytic coking behavior. No effect was observed on the asymptotic coking rates. Scanning electron microscopy, energy diffractive X-ray surface analysis, and cross section elemental mappings suggest that the effect of surface roughness and aging on the catalytic coking rate derives mainly from changes in the metal surface composition. The amounts of metallic Ni and Fe show an increasing tendency with increasing surface roughness, explaining the pronounced coke deposition. Using Ekvicalc, thermodynamic calculations were performed proposing that the amount of Cr2O3 gradually decreases followed by an increase of manganese chromite, MnCr2O4