A method of producing a fuel additive includes: passing a raffinate stream comprising C4 hydrocarbons through a hydrogenation unit, forming a first process stream; passing the first process stream through an extractive distillation unit, forming a C4 olefin stream; passing the C4 olefin stream through a stripper column, forming a purified C4 olefin stream; and forming the fuel additive by passing the purified C4 olefin stream through a hydration unit.

An erosion monitoring system of components exposed to wear for use in systems equipped with a fluidized catalyst comprising a bundle of fiber optic sensors, said optical fibers being provided with one or more Bragg gratings, a processing unit and the fiber optic sensors depart off from the bundle and are positioned transversely to the wall exposed to erosion wear due to the erosion of the components to be monitored.

Integrated systems are provided for the production of higher hydrocarbon compositions, for example liquid hydrocarbon compositions, from methane using an oxidative coupling of methane system to convert methane to ethylene, followed by conversion of ethylene to selectable higher hydrocarbon products. Integrated systems and processes are provided that process methane through to these higher hydrocarbon products.

Integrated systems are provided for the production of higher hydrocarbon compositions, for example liquid hydrocarbon compositions, from methane using an oxidative coupling of methane system to convert methane to ethylene, followed by conversion of ethylene to selectable higher hydrocarbon products. Integrated systems and processes are provided that process methane through to these higher hydrocarbon products.

A process for operating a thermal or catalytic cracking unit is described. The process entails generating a product that includes cracked hydrocarbon vapor and solid coke-particles from a heavy hydrocarbon input. The product is communicated towards a fractionator and a quench liquid is introduced into the product for creating a two-phase flow of cracked hydrocarbon vapor and the quench liquid with solid coke-particles entrained in the quench liquid. The two-phase flow is introduced into the fractionator and the cracked hydrocarbon vapor are separated from the quench liquid and the solid coke-particles entrained therein by gravity separation. The two-phase flow can reduce or remove the requirement of a wash zone within the fractionator. A recirculation loop is included in a wash-zone circulation system. The recirculation loop bypasses one or more spray headers of the wash zone and returns to a first end of the wash-zone circulation system.

A cyclic metals deactivation system unit for the production of equilibrium catalyst materials including a cracker vessel configured for cracking and stripping a catalyst material; and a regenerator vessel in fluid communication with the cracker vessel, the regenerator vessel configured for regeneration and steam deactivation of the catalyst material.

A cyclic metals deactivation system unit for the production of equilibrium catalyst materials including a cracker vessel configured for cracking and stripping a catalyst material; and a regenerator vessel in fluid communication with the cracker vessel, the regenerator vessel configured for regeneration and steam deactivation of the catalyst material.

The present disclosure describes systems, methods, and computer readable media that provide a hybrid approach that uses machine learning techniques and phenomenological reactor models for optimization of steam cracker units. While the phenomenological model allows capturing the physics of a steam cracker using molecular kinetics, the machine learning methods fill the gap between the phenomenological models and more detailed radical kinetics based steam cracker models. Also, machine learning based models can capture actual plant information and provide insight into the variation between the models and plant running conditions. The proposed methodology shows better interpolation and extrapolation capabilities as compared to stand-alone machine learning methods. Also, compared to detailed radical kinetics based models, the approach utilized in embodiments requires much less computational time in order to carry out whole plant-wide optimization or can be used for planning/scheduling purposes.

The present disclosure describes systems, methods, and computer readable media that provide a hybrid approach that uses machine learning techniques and phenomenological reactor models for optimization of steam cracker units. While the phenomenological model allows capturing the physics of a steam cracker using molecular kinetics, the machine learning methods fill the gap between the phenomenological models and more detailed radical kinetics based steam cracker models. Also, machine learning based models can capture actual plant information and provide insight into the variation between the models and plant running conditions. The proposed methodology shows better interpolation and extrapolation capabilities as compared to stand-alone machine learning methods. Also, compared to detailed radical kinetics based models, the approach utilized in embodiments requires much less computational time in order to carry out whole plant-wide optimization or can be used for planning/scheduling purposes.

A cyclic metals deactivation system unit for the production of equilibrium catalyst materials including a cracker vessel configured for cracking and stripping a catalyst material; and a regenerator vessel in fluid communication with the cracker vessel, the regenerator vessel configured for regeneration and steam deactivation of the catalyst material.