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.

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

2015 ◽  
Author(s):  
Dionysios P. Xenos ◽  
Erling Lunde ◽  
Nina F. Thornhill
Keyword(s):  
Large Scale ◽  
Optimal Schedule ◽  
Optimal Operation ◽  
Optimal Distribution ◽  
Compressor Station ◽  
Operational Costs ◽  
Operation And Maintenance ◽  
Multi Stage ◽  
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 multi-period 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 multi-stage 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 five days. Finally the results show the economical benefits from the integration of maintenance and optimization.

scholarly journals Research on Intelligent Operation and Maintenance Model of Large Database for Electric Power Industry

2021 ◽  
Vol 2095 (1) ◽  
pp. 012028
Author(s):  
Chaoyi Yang ◽  
Ye Liu ◽  
Jianyi Xiao ◽  
Xiaobo Huang ◽  
Jiahua Chen
Keyword(s):  
Large Scale ◽  
Performance Indicator ◽  
Electric Power Industry ◽  
Normal Operation ◽  
Model Framework ◽  
Intelligent Decision Making ◽  
Rule Model ◽  
Operation And Maintenance ◽  
Intelligent Decision ◽  
Maintenance Model

Abstract Existing research on automated database operation and maintenance for the electrical industry mainly focuses on distributed and cloud platforms, and there is a lack of traditional large-scale database intelligent operation and maintenance research. This paper designs an overall operation and maintenance model framework of “intelligent perception-intelligent decision-intelligent execution”, and proposes feasible implementation plans, including: (1) Use the prophet time series forecasting model to perceive and predict important database performance indicators, and dynamically adjust the threshold of each performance indicator according to the predicted value; (2) Perform correlation analysis on abnormal indicators through the association rule model to construct “Indicators”->Operation” optimized combination operation strategy library for intelligent decision making; (3) According to the intelligent decision library, automatically restrict the associated operations under abnormal conditions to ensure the normal operation of the service and realize intelligent execution.

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 %.

Production System Optimal Operation Model Based on Large-Scale System Theory

2010 ◽  
Vol 148-149 ◽  
pp. 47-52
Author(s):  
Zhi Guo Wang
Keyword(s):  
Optimization Model ◽  
Production System ◽  
Large Scale ◽  
Optimal Operation ◽  
Solution Algorithm ◽  
Operation Model ◽  
System Operation ◽  
Operation Optimization ◽  
Time Dynamic ◽  
Energy Meter

For the disadvantage of traditional production mode, an operation optimization model is put forward on the basis of analyzing workshop production system operation. The Features of the model are real-time, dynamic, multi-objective and multistage. The algorithm called ‘Harmonizing Cluster Results’ for the model is presented in order to solve the difficulties of solution for the workshop production system operation model. Then the model is simplified into some submodels, such as production ability optimization submodel, time optimization submodel, balancing and harmonizing submodel etc. Finally the operation optimization model and its solution algorithm are applied to an assembly line of energy meter in a factory. With the help of ‘Witness’ the application case simulation results showed that the model is accurate and its solution algorithm is reasonable and available.

scholarly journals Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 625
Author(s):  
Xinyu Wu ◽  
Rui Guo ◽  
Xilong Cheng ◽  
Chuntian Cheng
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Large Scale ◽  
Simulation Optimization ◽  
Optimal Operation ◽  
Stochastic Dynamic ◽  
Operation Model ◽  
Two Stage ◽  
Operation Rules ◽  
Reservoir Systems

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

Minimization of Energy Using Heuristic Resource Allocation and Migration for Cloud Computing

2021 ◽  
Vol 12 (1) ◽  
pp. 74-83
Author(s):  
Manjunatha S. ◽  
Suresh L.
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Service Providers ◽  
Cost Effective ◽  
Cooling Systems ◽  
Operational Costs ◽  
Cloud Computing Service ◽  
And Migration

Data center is a cost-effective infrastructure for storing large volumes of data and hosting large-scale service applications. Cloud computing service providers are rapidly deploying data centers across the world with a huge number of servers and switches. These data centers consume significant amounts of energy, contributing to high operational costs. Thus, optimizing the energy consumption of servers and networks in data centers can reduce operational costs. In a data center, power consumption is mainly due to servers, networking devices, and cooling systems, and an effective energy-saving strategy is to consolidate the computation and communication into a smaller number of servers and network devices and then power off as many unneeded servers and network devices as possible.

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