The present invention relates to a computer-implemented method for monitoring and/or controlling a chemical plant. Specifically, the present invention relates to a computer-implemented method for monitoring and/or controlling a physical-chemical process in a chemical plant comprising: (a) receiving sensor data related to the physical-chemical process, (b) determining at least one physical-chemical parameter by providing the sensor data to a plant model, wherein the plant model comprises a mechanistic model containing at least two equations each representing a part of the physical-chemical process and a data-driven model associated to the mechanistic model, wherein the total number of scalars as output parameters from the data-driven model is lower than the number of equations of the mechanistic model, and (c) outputting the at least one physical-chemical parameter determined by the plant model.

Assemblies and methods to enhance control of a fluid catalytic cracking (FCC) processing assembly associated with a refining operation, may include supplying a hydrocarbon feedstock to one or more first processing units associated with the refining operation. The assemblies and methods also may include conditioning a hydrocarbon feedstock and unit material samples, and analyzing the samples via one or more spectroscopic analyzers. The assemblies and methods further may include prescriptively controlling, via one or more FCC process controllers based at least in part on the hydrocarbon feedstock properties and the unit material properties, the FCC processing assembly, so that the prescriptively controlling results in enhancing accuracy of target content of materials produced by the FCC processing assembly, thereby to more responsively control the FCC processing assembly to achieve material outputs that more accurately and responsively converge on target properties.

A method of determining multimodal polyethylene quality comprising the steps of (a) providing a multimodal polyethylene resin sample; (b) determining, in any sequence, the following: that the multimodal polyethylene resin sample has a melt index within 30% of a target melt index; that the multimodal polyethylene resin sample has a density within 2.5% of a target density; that the multimodal polyethylene resin sample has a dynamic viscosity deviation (% MVD) from a target dynamic viscosity of less than about 100%; that the multimodal polyethylene resin sample has a weight average molecular weight (M.sub.w) deviation (% M.sub.wD) from a target M.sub.w of less than about 20%; and that the multimodal polyethylene resin sample has a gel permeation chromatography (GPC) curve profile deviation (% GPCD) from a target GPC curve profile of less than about 15%; and (c) responsive to step (b), designating the multimodal polyethylene resin sample as a high quality resin.

A foam sensor device is used for monitoring foam within a vessel. The sensor (e.g. accelerometer) is encapsulated inside a water-tight, sterilizable, shell, which floats on a liquid contained. In one example, the foam sensor device includes an accelerometer for detecting and measuring rotation and movement of the foam sensor device and generates movement data based on the detected movement. During a learning or calibration process, sensor data (e.g., movement data) from the foam sensor device is analyzed and classified using machine learning and/or signal processing methods to extract features indicative of different possible foam statuses, including varying levels of foam, or no foam and generate models for the different statuses. During normal operation, the foam sensor device transmits sensor data to an analyzer containing the pre-calibrated models, which determines whether there is foam or not. Based on the foam status, a pump controller adds anti-foam solution.