Systems and methods associated with biomass decomposition are generally described. Certain embodiments are related to adjusting a flow rate of a fluid comprising oxygen into a reactor in which biomass is decomposed. The adjustment may be made, at least in part, based upon a measurement of a characteristic of the reactor and/or a characteristic of the biomass. Certain embodiments are related to cooling at least partially decomposed biomass. The biomass may be cooled by flowing a gas over an outlet conduit in which the biomass is cooled, and then directing the gas to a reactor after it has flowed over the outlet conduit. Certain embodiments are related to systems comprising a reactor and an outlet conduit configured such that greater than or equal to 75% of its axially projected cross-sectional area is occupied by a conveyor. Certain embodiments are related to systems comprising a reactor comprising an elongated compartment having a longitudinal axis arranged substantially vertically and an outlet conduit comprising a conveyor.

By this invention processes are provided for the conversion of carbohydrate to ethylene glycol by retro-aldol catalysis and sequential hydrogenation using control methods having at least one of acetol (hydroxyacetone) and a tracer as inputs.

Control methods are disclosed for continuous, unmodulated, multiple catalytic conversion step processes using at least two catalysts, a first catalyst and a second catalyst, that accommodate changes in the performance of each catalyst and the relative performances of the catalysts. In the methods, certain process parameters are used in a manner that is indicative of changes in catalyst performance, and the control methods provide for adjustment of at least one of: the absolute amount of catalytically active species and relative amounts of each of the first catalyst and second catalyst and at least one of the rate of feed or concentration of the raw material to the reaction zone.

Computer-based process modeling and simulation methods and systems combine first principles models and machine learning models to benefit where either model is lacking. In one example, input values (measurements) are adjusted by first principles techniques. A machine learning model of the chemical process of interest is trained on the adjusted values. In another example, a machine learning model represents the residual (delta) between a first principles model prediction and empirical data. Residual machine learning models correct physical phenomena predictions in a first principles model of the chemical process. In another example, a first principles simulation model uses the process input data and predictions of the machine learning model to generate simulated results of the chemical process. The hybrid models enable a process engineer to troubleshoot the chemical process, enable debottlenecking the chemical process, enable optimizing performance of the chemical process at the subject industrial plant, and enable automated process control.

Various embodiments of the present disclosure relate to proactive dosing optimization chemical feed units producing an output solution (such as an oxidizing biocide) therefrom. Online sensors generate signals corresponding to directly measured variables for respective process components. Information is selectively retrieved from models relating combinations of input variables to respective industrial process states, wherein various current process states may be indirectly determined based on directly measured variables for respective system components. An output feedback signal is automatically generated corresponding to a detected intervention event based on the indirectly determined process state. A controller may receive the signal and implement, e.g., regulation of oxidizing biocide feed for optimization of end products and/or performance metrics.

Techniques regarding the synthesis of one or more polymers of a target polymer class are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a recommendation component that can generate a recommended chemical reactor control setting for inverse synthesis of a polymer based on a target polymer characteristic and reactor training data.

Computer-based process modeling and simulation methods and systems combine first principles models and machine learning models to benefit where either model is lacking. In one example, input values (measurements) are adjusted by first principles techniques. A machine learning model of the chemical process of interest is trained on the adjusted values. In another example, a machine learning model represents the residual (delta) between a first principles model prediction and empirical data. Residual machine learning models correct physical phenomena predictions in a first principles model of the chemical process. In another example, a first principles simulation model uses the process input data and predictions of the machine learning model to generate simulated results of the chemical process. The hybrid models enable a process engineer to troubleshoot the chemical process, enable debottlenecking the chemical process, enable optimizing performance of the chemical process at the subject industrial plant, and enable automated process control.

The invention relates to a virtual sensing method and system for controlling at least one composition variable in a urea production process, based on a plurality of online measured process variables and a model, wherein the model is used to estimate, during the urea production process, the at least one composition variable indicative of a urea content on the basis of the plurality of online measured process variables, and modifying at least one of the plurality of online measured process variables for ensuring that a value of the at least one composition variable is within a predetermined range. The invention also relates to determining the model.

The present invention relates to a method of catalytic process characterization which comprises a reaction system having two or more reaction strands in a parallel arrangement, wherein an individual reaction strand comprises multiple series-connected reaction chambers or a single reaction chamber. In the method, which is also referred to as CPC method, each reaction strand is supplied with a reactant stream. The reactant streams supplied to the reaction strands are subjected to different numbers of process stages in the different reaction strands. The product streams discharged from the reaction strands are subjected to an analytical characterization, wherein the data achieved in the characterization are expressed in relative terms, here preferably including the forming of a difference. The CPC method can be used in a very versatile manner and is characterized by very high accuracy. The mass balance achieves a standard deviation of +/10% by weight or lower. Furthermore, the invention relates to an apparatus for performing the CPC method or else to an apparatus for simultaneously performing a multitude of CPC methods. The invention thus also relates to the field of high-throughput research.

A method and an apparatus for detailed continuous monitoring of the thermal environment for a tube or a plurality of tubes and calculation and prediction of remaining lifetime of said tubes.