A method for separating dimethyl carbonate from methanol includes subjecting a raw material containing dimethyl carbonate and methanol to extractive distillation, wherein an extractant comprising a compound having a general formula of CH.sub.3O(CH.sub.2CH.sub.2O).sub.nCH.sub.3, n being an integer of 2-8, is used in the extractive distillation. The method has the advantages of good separation effect, high product purity, and environmental friendly.

Certain aspects of natural gas liquid fractionation plant waste heat conversion to simultaneous power and potable water using Kalina Cycle and modified multi-effect-distillation system can be implemented as a system. The system includes a waste heat recovery heat exchanger network coupled to multiple heat sources of a Natural Gas Liquid (NGL) fractionation plant. The heat exchanger network is configured to transfer at least a portion of heat generated at the multiple heat sources to a first buffer fluid and a second buffer fluid flowed through the first heat exchanger network. The system includes a first sub-system configured to generate power. The first sub-system is thermally coupled to the waste heat recovery heat exchanger. The system includes a second sub-system configured to generate potable water from brackish water. The second sub-system is thermally coupled to the waste heat recovery heat exchanger.

The invention relates to a method for predicting flooding in a distillation column by machine learning including a constructing and training phase of a machine learning model obtained from previously collected data and from a set of sensors, an operational phase for predicting flooding(s), by collecting a current data flow until a buffer is filled, pre-processing data from the data buffer by predetermined cleansing and classification, synchronizing the data of the current set of clean and classified data, determining a value of a current variable representative of at least one current performance of the at least one distillation column, forming a current set of transformed data by calculating predetermined derivatives, and predicting the current state of said distillation column by applying said learning model to said current set of transformed data.

A diaryl carbonate containing a compound of the following formula (I) in an amount of less than 1,000 ppm by mass, and a method for producing the same:

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wherein R.sup.1 represents a hydrogen atom, a halogen atom, an alkyl group, an alkoxy group, an aryl group, or an aryloxy group.

Systems for purifying 1,3-butadiene are provided. An exemplary system includes a first distillation column, a second distillation column, and interconnections between the first and second distillation columns. A first interconnection can feed a liquid stream from the second distillation column to the first, while a second interconnection can feed a gas stream from the second distillation column to the first. Processes for purifying 1,3-butadiene are also provided.

Systems for purifying 1,3-butadiene are provided. An exemplary system includes a first distillation column, a second distillation column, and interconnections between the first and second distillation columns. A first interconnection can feed a liquid stream from the second distillation column to the first, while a second interconnection can feed a gas stream from the second distillation column to the first. Processes for purifying 1,3-butadiene are also provided.

The present invention relates to a method of monitoring a system, comprising the following steps: i. obtaining time series describing the temporal evolution of a parameter of a system between an initial and a final time, the final time being triggered by the occurrence of an abnormal event affecting the system, ii. for each time series, defining an abnormal time period and a normal time period, iii. for each time series, determining a metric characterizing each parameter of the series under consideration over the abnormal time period and over the normal time period, iv. determining association rules on the basis of the characterizing metrics, v. validating the obtained association rules, and vi. the predicting the occurrence or lack of occurrence of an abnormal event liable to affect a system to be monitored on the basis of the validated association rules.

Embodiments presented provide for a multirotational counter rotating reactor. The reactor is configured to accept a fluid stream and separate the fluid stream into high quality liquid and gaseous phases through spinning of the sets of discs as well as through performing a heat transfer to the fluid stream.

A method of real-time prognosis of a flooding phenomenon in a packed column includes steps as follows. An online data collection step is conducted, wherein a plurality of values of a pressure drop are collected from the packed column under operation. A detrending step is conducted to obtain a plurality of values of a detrended pressure drop. A fitting step is conducted, wherein the values of the detrended pressure drop are fitted with an EGARCH(p, q) model to obtain a value of at least one of model coefficients. A repeat step is conducted to obtain another value of the at least one of model coefficients. A statistical step is conducted, wherein a value of the monitoring statistic is calculated. A control step is conducted, wherein the value of the monitoring statistic is compared to a control limit.

A method of real-time prognosis of a flooding phenomenon in a packed column includes steps as follows. An online data collection step is conducted, wherein a plurality of values of the pressure drop are collected from the packed column under operation. A calculation step is conducted, wherein the values of the pressure drop are used to calculate a plurality of values of a steadiness index. A statistical step is conducted, wherein a value of a monitoring statistic is calculated based on the values of the steadiness index. A control step is conducted, wherein the value of the monitoring statistic is compared to a control limit, and an alarm is triggered when the value of the monitoring statistic is greater than the control limit.