SABIC Global Technologies

The disclosure provides methods and systems for producing chemicals and fuels from gas condensates. One such method includes distilling a gas condensate feedstock to produce a light product stream having C₂-C₄ compounds, a first naphtha stream, a distillate stream, and a heavy residue stream, hydrotreating the distillate stream to produce a hydrotreated distillate stream, distilling the heavy residue stream under vacuum pressure to produce a gas oil stream, hydrocracking the gas oil stream to produce a second naphtha stream and an unconverted oil stream, and steam cracking the light product stream, the first naphtha stream, the hydrotreated distillate stream, the second naphtha stream, and the unconverted oil stream to produce one or more high value chemical product streams comprising ethene and propene.

Foams produced from repeatedly-recyclable polyolefin-based copolymer mimics are described.

A method for hydroprocessing a heavy hydrocarbon feed in a hydroprocessing reactor that includes a partially deactivated fixed bed hydroprocessing catalyst is disclosed. The method can include contacting at least a portion of the heavy hydrocarbon feed and hydrogen (H₂) with a demetallization catalyst to obtain a demetallized hydrocarbon stream and contacting at least a portion of the demetallized hydrocarbon stream and a dissolved transition metal catalyst with the partially deactivated fixed bed hydroprocessing catalyst to produce a hydroprocessed hydrocarbon stream and an active fixed bed hydroprocessing catalyst.

Disclosed is a system and method for producing a steam cracker feed. The method can include performing a first hydrogenolysis of at least a portion of a hydrotreated hydrocarbon composition in a first hydrogenolysis reactor using a non-noble metal catalyst to form a first hydrogenolyted hydrocarbon composition; and performing a second hydrogenolysis of at least a portion of the first hydrogenolyted hydrocarbon composition in a second hydrogenolysis reactor using a noble metal catalyst at a temperature lower than that of the first hydrogenolysis to form a steam cracker feed, where the steam cracker feed comprises an iso-paraffin/n-paraffin weight ratio of less than 1.

The present invention relates to an article comprising a carrier provided at least in part with a coating layer, wherein - said carrier comprises or consists of a thermoplastic composition, said thermoplastic composition comprising polycarbonate, - said coating layer is applied directly on said carrier and comprises or consists of polyurethane obtained by reacting at least one poly-isocyanate and at least one polyol, - said thermoplastic composition comprises a hydroxy compound selected from the group consisting of a polyol compound represented by formula (I): HO-(R<1>X)<m>-A-(R<2>Y)<n>-H (I) wherein R<1> and R<2> each independently represent an alkylene group having 1 or more carbon atoms, X and Y each independently represent oxygen, an ester group or a carbonate group, m and n each independently represent an integer, m+n is 5 to 500, and A represents a single bond or a divalent organic group and an acrylic compound represented by formula (II): H-(CH<2>-C(R<3>)(C=O)OR<4>)<p>-(CH<2>-C(R<5>)(R<6>OH))<q>-(CH<2>-C(R<7>)(R<8>))<r>-H (II) wherein R<3>, R<5>, and R<7> each independently represent H or an alkylene group having 1 or more carbon atoms, R<4>, and R<6> each independently represent an alkylene group having 1 or more carbon atoms, R<8> represents an aromatic group, p represents an integer, q represents an integer of at least 1, r represents an integer and p+q+r is 5 to 500 and combinations thereof, wherein the amount of the hydroxy compound is 0.1 to 1.0 wt.% based on the weight of the thermoplastic composition.

The invention relates to a method for producing monoalkylene glycol, the method comprising: a) flowing a feed stream comprising alkylene oxide and water into a first reactor unit; b) subjecting the feed stream, in the first reactor unit, to first reaction conditions to obtain a first effluent comprising monoalkylene glycol, unreacted alkylene oxide and unreacted water, wherein the first reaction conditions in the first reactor unit are non-catalytic reaction conditions; c) flowing at least a portion of the first effluent to a second reactor unit; and d) subjecting said portion of the first effluent, in the second reactor unit, to second reaction conditions to obtain a second effluent comprising monoalkylene glycol, wherein the second reactor unit comprises a reactor comprising a catalyst bed and the second reaction conditions in the second reactor unit are catalytic reaction conditions.

Disclosed here are methods and systems to determine asset status in an industrial plant. Embodiments of the method may include, during operation of an asset positioned at the industrial plant and, iteratively, at each of a plurality of selected time intervals, receiving a plurality of data streams from sensors or devices of the asset. The method may include determining, based on application of each of the plurality of pre-processed data streams received to each of a plurality of trained machine learning models, anomaly scores. The method may include determining, via a plurality of votes based on the anomaly scores, a status for the sensors and/or devices. The method may include, if the status for the sensors and/or devices for the asset indicates an anomaly for a consecutive two or more of the plurality of time intervals, generating a next action notification to indicate the anomaly and prevent unplanned downtime.

A dynamic catalyst lifecycle model is described. In some embodiments, real-time external data associated with a catalyst is captured. Actual operational data associated with a petrochemical plant is obtained and the catalyst is used in chemical processes at the petrochemical plant. A selectivity of the catalyst is continuously predicted under different loads and in real-time using a trained machine learning model. The actual operational data and the real-time external data are used as inputs to the trained machine learning model and the trained machine learning model is trained using an actual selectivity and historical catalyst performance.

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