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

New trends in Olefin production

Most olefins (e.g., ethylene and propylene) will continue to be produced through steam cracking (SC) of hydrocarbons in the coming decade. In an uncertain commodity market, the chemical industry is investing very little in alternative technologies and feedstocks because of their current lack of economic viability, despite decreasing crude oil reserves and the recognition of global warming. In this perspective, some of the most promising alternatives are compared with the conventional SC process, and the major bottlenecks of each of the competing processes are highlighted. These technologies emerge especially from the abundance of cheap propane, ethane, and methane from shale gas and stranded gas. From an economic point of view, methane is an interesting starting material, if chemicals can be produced from it. The huge availability of crude oil and the expected substantial decline in the demand for fuels imply that the future for proven technologies such as Fischer-Tropsch synthesis (FTS) or methanol to gasoline is not bright. The abundance of cheap ethane and the large availability of crude oil, on the other hand, have caused the SC industry to shift to these two extremes, making room for the on-purpose production of light olefins, such as by the catalytic dehydrogenation of propane

Sustainable innovations in steam cracking : CO2 neutral olefin production

Steam cracking of hydrocarbons is and will continue to be the main industrial process to produce light olefins in the coming decades. In state of the art steam cracking plants more than 90% of the CO2 emissions can be directly related to the high energy consumption of the endothermic conversion in the cracking furnaces. Steam cracking accounts for a global emission of more than to 300 million tonnes of CO(2)per annum. Enhancing heat transfer in the radiation section, using green energy and reducing coke formation are key to substantially reduce CO2 emissions. Heat transfer can be increased by implementing three-dimensional (3D) coil technologies such as swirled and dimpled tubes. These reactor technologies also reduces coke formation because of the lower wall temperatures that are consequently obtained. Advanced manufacturing techniques and better computational abilities have opened the door to novel and improved 3D reactor technologies that are designed to increase the heat transfer while minimizing the pressure drop penalty. At the same time applying high emissivity coatings on the furnace refractory and reactor tubes can further reduce CO2 emissions. Substantial fuel savings can also be obtained by a novel furnace design, where the heat recovery scheme is substantially modified. Combining all these technologies could result in reducing emissions by 30%. Shifting completely to green electricity, which is practically infeasible today, is another alternative but the technologies that would potentially allow this are still in their infancy. These new technologies, combined with advanced process innovations and CO2 capture, will help the industry to meet future emissions targets

Steam cracking of bio-derived normal and branched alkanes : influence of branching on product distribution and formation of aromatics

The presence of large amounts of oxygen in the molecular structure of triglyceride and fatty acid based feedstocks makes direct use in conventional steam crackers impossible without substantial modifications to the cold section. Full or partial catalytic deoxygenation has potential to resolve this, giving a mixture which consists primarily of normal and branched alkanes. Two ofthese deoxygenated mixtures have been investigated theoretically and experimentally in a dedicated bench setup (P = 0.17 MPa, T = 1050–1150K, FHC = 4.17 10−2 g s−1, steam dilution of 0.3 and 0.5 gH2O/gHC). Furthermore, the degree of branching of the hydrocarbon mixtures impacts the product distribution, in particular the alkene selectivity. The newly generated, validated detailed kinetic model shows that small alkenes are formed by hydrogen abstraction and successive C C -scission reactions. In the studied temperature range mono-aromatics are formed by three competing pathways: a series of recombination reactions of allylic radicals followed by hydrogen abstraction and intramolecular radical additions, additions of allylic and vinyl radicals on dienes followed by intramolecular radical addition, and finally recombination reactions of carbon-centered radicals with 1,3-cyclopentadienyl followed by hydrogen abstraction and ring enlargement