Оценка эффективности рециркуляционной технологии использования метанола при подготовке газа методом низкотемпературной сепарации

Объектом исследования является технология подготовки газа на Мыльджинском газоконденсатном месторождении. Цель данной работы – повышение эффективности использования метанола при подготовке газа методом низкотемпературной сепарации. С помощью моделирования в среде программы UniSim Design проведено исследование вариантов реализации рециркуляционной технологии использования метанола на УКПГ Мыльджинского месторождения. В результате исследования выявлено, что предлагаемый вариант рециркуляционной технологии позволяет сократить расход метанола на 110–140 кг/ч (40–45 %) за счет уменьшения уноса метанола с нестабильным конденсатом. Добавленная в технологическую схему установка ректификации метанола позволяет вернуть в технологический процесс еще 85–120 кг/ч метанола.The object of the study is the gas preparation technology at the Myldzhinskoye gas condensate field. The goal of this work is to increase the efficiency of methanol use in gas preparation by the low-temperature separation method. Using simulation in the UniSim Design program environment, a study was carried out on options for the implementation of the recirculation technology for the use of methanol at the gas treatment unit at the Myldzhinskoye field. As a result of the study, it was found that the proposed version of the recirculation technology reduces methanol consumption by 110–140 kg / h (40–45%) by reducing the entrainment of methanol with unstable condensate. The methanol rectification unit allows to return another 85–120 kg / h of methanol to the process

An Integrated Pipeline for the Genome-Wide Analysis of Transcription Factor Binding Sites from ChIP-Seq

ChIP-Seq has become the standard method for genome-wide profiling DNA association of transcription factors. To simplify analyzing and interpreting ChIP-Seq data, which typically involves using multiple applications, we describe an integrated, open source, R-based analysis pipeline. The pipeline addresses data input, peak detection, sequence and motif analysis, visualization, and data export, and can readily be extended via other R and Bioconductor packages. Using a standard multicore computer, it can be used with datasets consisting of tens of thousands of enriched regions. We demonstrate its effectiveness on published human ChIP-Seq datasets for FOXA1, ER, CTCF and STAT1, where it detected co-occurring motifs that were consistent with the literature but not detected by other methods. Our pipeline provides the first complete set of Bioconductor tools for sequence and motif analysis of ChIP-Seq and ChIP-chip data