Modeling and Optimization of Industrial Centrifugal Compressor Stations Employing Data-Driven Methods

2014 ◽  
Author(s):  
Dionysios P. Xenos ◽  
Matteo Cicciotti ◽  
Ala E. F. Bouaswaig ◽  
Nina F. Thornhill ◽  
Ricardo Martinez-Botas
Keyword(s):  
Power Consumption ◽  
Optimization Model ◽  
Cooling Water ◽  
Health Condition ◽  
Optimal Operation ◽  
Data Driven ◽  
Compressor Station ◽  
Real Process ◽  
Parallel Configuration ◽  
The Individual

This paper addresses optimal operation of centrifugal compressors operating in a parallel configuration. A group of compressors operates in parallel to increase the supply of a gas. One current practice is to distribute the load equally without considering the fact that the individual compressors have different characteristics and they are in a different health condition due to past hours of operation and non-uniform maintenance plan. Data from past operation is used for generating data-driven models of the compressors. These models and operational constraints of the compressor station are used in an optimization model. The optimization model computes the distribution of both the load and cooling water of each compressor which reduces the operational cost, i.e. power consumption of the motors and purchase of cooling water. The suggested optimization model is applied on a real process case study. The results from optimal operation from optimization show a reduction in the power consumption of the compressor station compared to the actual power consumed in past operation. The magnitude of this benefit ranges between 0.67 up to 2.16 %.

Optimal Operation and Maintenance of Gas Compressor Stations: An Integrated Framework Applied to a Large-Scale Industrial Case

2015 ◽  
Vol 138 (4) ◽  
Author(s):  
Dionysios P. Xenos ◽  
Erling Lunde ◽  
Nina F. Thornhill
Keyword(s):  
Optimization Model ◽  
Large Scale ◽  
Optimal Schedule ◽  
Optimal Operation ◽  
Optimal Distribution ◽  
Compressor Station ◽  
Operational Costs ◽  
Operation And Maintenance ◽  
Maintenance Model ◽  
Operational Aspects

This paper presents a framework which integrates maintenance and optimal operation of multiple compressors. The outcome of this framework is a multiperiod plan which provides the schedule of the operation of compressors: the schedule gives the best decisions to be taken, for example, when to carry out maintenance, which compressors to use online and how much to load them. These decisions result in the minimization of the total operational costs of the compressors while at the same time the demand of the plant is met. The suggested framework is applied to an industrial gas compressor station which encompasses large multistage centrifugal compressors operating in parallel. The optimization model of the framework consists of three main parts: the models of compressor maps, the operational aspects of compressors, and a maintenance model. The results illustrate the optimal schedule for 90 days and an example of the optimal distribution of the load of the compressors for 5 days. Finally, the results show the economical benefits from the integration of maintenance and optimization.

scholarly journals A novel collective health monitoring of a wind park

2021 ◽  
Vol 21 (2) ◽  
pp. 625
Author(s):  
Kritika Sodha ◽  
George Fernandez S. ◽  
Vijayakumar K. ◽  
Sattianadan D.
Keyword(s):  
Condition Monitoring ◽  
Communication Systems ◽  
Health Condition ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Maintenance Schedule ◽  
Data Driven Approach ◽  
Condition Monitoring System ◽  
Using Data ◽  
The Individual

<p>Compared to a time-based maintenance schedule, condition-based maintenance provides better diagnostic information on the health condition of the different wind turbine components and subsystems. Rather than using an offline condition monitoring technique, which require the WT to be taken out of service, online condition monitoring does not require any interruption on the WT operation. The online condition monitoring system uses different types of sensors such as vibration, acoustic, temperature, current/voltage etc. Using a machine learning approach, we aim to establish a data driven fault prognosis framework. Instead of traditional wired communications, wireless communication systems such as Wireless Sensor Network have the advantages of easier installation and lower capital cost. We propose the use of WSN for collecting and transmitting the condition monitoring data to enhance the reliability of Wind Parks. Using data driven approach the collective health of the WP can be represented based on the condition of the individual wind turbines, which can be used for predicting the Remaining Useful Life of the system.</p>

Construction of citizenship in hospitalized children, Peru

2019 ◽  
Vol 1 (1) ◽  
pp. 31
Author(s):  
Fernando Ledesma Perez ◽  
Maria Caycho Avalos ◽  
Juana Cruz Montero ◽  
Andrea Ayala Sandoval
Keyword(s):  
Structured Interview ◽  
Health Condition ◽  
Qualitative Approach ◽  
Fundamental Rights ◽  
Hospitalized Children ◽  
Health Institute ◽  
Interview Guide ◽  
Communication Interaction ◽  
The Individual

Citizenship is the exercise of the fundamental rights of people in spaces of participation, opinion and commitments, which can not be violated by any health condition in which the individual is. This research aims to interpret the process of construction of citizenship in hospitalized children, was developed through the qualitative approach, ethnomethodological method, synchronous design, with a sample of three students hospitalized in a health institute specializing in childhood, was used Observation technique and a semi-structured interview guide were obtained as results that hospitalized children carry out their citizenship construction in an incipient way, through the communication interaction they make with other people in the environment where they grow up.

Data-driven health condition and RUL prognosis for liquid filtration systems

2021 ◽  
Vol 35 (4) ◽  
pp. 1597-1607
Author(s):  
Seunghyun Lee ◽  
Seungju Lee ◽  
Kwonneung Lee ◽  
Sangwon Lee ◽  
Jaemin Chung ◽  
...  
Keyword(s):  
Health Condition ◽  
Data Driven ◽  
Liquid Filtration

Data Science and real world health data improve precision for diagnosis: Acute Kidney Injury, acute-on-chronic and Chronic Kidney Disease (Preprint)

2021 ◽  
Author(s):  
Karen Triep ◽  
Alexander Benedikt Leichtle ◽  
Martin Meister ◽  
Georg Martin Fiedler ◽  
Olga Endrich
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Data Science ◽  
Query Language ◽  
Correct Diagnosis ◽  
Kidney Injury ◽  
Data Driven ◽  
World Health ◽  
Discharge Data ◽  
The Individual

BACKGROUND The criteria for the diagnosis of kidney disease outlined in “The Kidney Disease: Improving Global Outcomes (KDIGO)” are based on a patient’s current, historical and baseline data. The diagnosis of acute (AKI), chronic (CKD) and acute-on-chronic kidney disease requires past measurements of creatinine and back-calculation and the interpretation of several laboratory values over a certain period. Diagnosis may be hindered by unclear definition of the individual creatinine baseline and rough ranges of norm values set without adjustment for age, ethnicity, comorbidities and treatment. Classification of the correct diagnosis and the sufficient staging improves coding, data quality, reimbursement, the choice of therapeutic approach and the patient’s outcome. OBJECTIVE With the help of a complex rule-engine a data-driven approach to assign the diagnoses acute, chronic and acute-on-chronic kidney disease is applied. METHODS Real-time and retrospective data from the hospital’s Clinical Data Warehouse of in- and outpatient cases treated between 2014 – 2019 is used. Delta serum creatinine, baseline values and admission and discharge data are analyzed. A KDIGO based standard query language (SQL) algorithm applies specific diagnosis (ICD) codes to inpatient stays. To measure the effect on diagnosis, Text Mining on discharge documentation is conducted. RESULTS We show that this approach yields an increased number of diagnoses as well as higher precision in documentation and coding (unspecific diagnosis ICD N19* coded in % of N19 generated 17.8 in 2016, 3.3 in 2019). CONCLUSIONS Our data-driven method supports the process and reliability of diagnosis and staging and improves the quality of documentation and data. Measuring patients’ outcome will be the next step of the project.

scholarly journals COMBINING DATA-DRIVEN AND NUMERICAL MODELLING APPROACHES TO STORM EROSION PREDICTION

2020 ◽  
pp. 38
Author(s):  
Joshua Simmons ◽  
Kristen Splinter
Keyword(s):  
Numerical Models ◽  
Individual Performance ◽  
Study Data ◽  
Data Driven ◽  
Erosion Prediction ◽  
Trade Offs ◽  
Combining Data ◽  
Erosion Models ◽  
Using Data ◽  
The Individual

Physics-based numerical models play an important role in the estimation of storm erosion, particularly at beaches for which there is little historical data. However, the increasing availability of pre-and post-storm data for multiple events and at a number of beaches around the world has opened the possibility of using data-driven approaches for erosion prediction. Both physics-based and purely data-driven approaches have inherent strengths and weaknesses in their ability to predict storm-induced erosion. It is vital that coastal managers and modelers are aware of these trade-offs as well as methods to maximise the value from each modelling approach in an increasingly data-rich environment. In this study, data from approximately 40 years of coastal monitoring at Narrabeen-Collaroy Beach (SE Australia)has been used to evaluate the individual performance of the numerical erosion models SBEACH and XBeach, and a data-driven modelling technique. The models are then combined using a simple weighting technique to provide a hybrid estimate of erosion.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/v53dZiO8Y60

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