The present invention relates to a method to control a liquid feed stream carried out by measuring at least one spectrum of the liquid feed stream comprising an initiator or catalyst, determining its activity using a predictive model on the basis of said spectrum and adjusting the feed streams and/or the preparation conditions of the feed stream comprising the initiators or catalysts in order to attain or maintain a desired level of total activity. In a further aspect the invention relates to a controlled process for the preparation of polymers in a polymerization reactor by contacting liquid feed streams comprising monomers and initiators or catalysts, whereby the control of the feed streams entering the polymerization reactor and/or the initiator or catalyst preparation is carried out by measuring at least one spectrum of the liquid feed stream comprising an initiator or catalyst, determining its activity using a predictive model on the basis of said spectrum and adjusting the feed streams and/or the preparation conditions of the feed stream comprising the initiators or catalysts in order to attain or maintain a desired level of total activity within the polymerization reactor. More particularly, the invention relates to a method for controlling the activity of aluminum containing initiators in a process for the co-polymerization of isolefins and multiolefins, in particular isobutylene and isoprene. The present invention further to device and a chemical plant suitable to operate said process.

Vapor delivery apparatus configured for generating a gaseous precursor from solid source precursor particles in a fluidized bed are disclosed. In addition, vapor phase reactors including a vapor delivery apparatus including a fluidized bed of solid precursor are also disclosed. Methods for monitoring and a controlling a vapor delivery system including a fluidized bed also disclosed.

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.