Query: chiller operation in series

Candidate papers:

abstract: In semiconductor and electronics factories, large multi chiller systems are needed to satisfy strict cooling load requirements. In order to save energy, it is worthwhile to design the chilled water system operation. In this paper, an optimal flexible operation scheme is developed based on a two dimensional time series model to forecast the cooling load of multi chiller systems with chiller units of different cooling capacities running in parallel. The optimal integrity scheme can be obtained using the Mixed Integer Nonlinear Programming method, which minimizes the energy consumption of the system within a future time period. In order to better adapt the change of cooling load, the operation strategy of regulating the chilled water flowrates is employed. The chilled water flowrates are set as a design variable. When the chillers are running, their chilled water flowrates can vary within limits, whereas the flowrates are zero when the chillers are unloaded. This forecasting method provides integral optimization within a future time period and offers the operating reference for operators. The power and advantages of the proposed method are presented using an industrial case to help readers delve into this matter.
author_names: Yan-yue Lu

Junghui Chen

Yuan-bin Mo
title: Optimal Operation Strategies for a Large Multi Chiller System Based on Cooling Load Forecast
year: 2019

abstract: Currently, increased clean energy demand due to rapid industrialization and population growth is the main challenge of the world. This can be achieved either by energy efficiency improvement for both the house hold appliances and industrial processes or by utilizing alternative energy resources or fuels. Energy efficiency of an industrial plant may be improved by optimizing its design and operation. Utilization of waste heat may also be an important option for the energy efficiency improvement of the industrial processes. Vapor absorption chillers (VAC) commonly known as absorption refrigeration system (ARS) can utilize the low quality energy or waste heat (which otherwise may have been wasted) as compared to the conventional vapor compression cycles (VCC) VCC require large amount of electricity for compression of the refrigerant. Furthermore, other benefits of VAC over VCC include the use of safe working fluids, low maintenance cost and low noise pollution. Though, low efficiency of VAC is the major issue associated with it. Therefore, the efficiency improvement becomes an importance factor and it can be achieved by analyzing and optimizing the various process configurations of the cycle. In this work, performance of VAC is specified as exergy efficiency instead of conventional coefficient of performance approach. Both the series and parallel flow configurations have been considered. Various operating parameters such as temperatures of all the units of both configurations have been optimized. The thermodynamic model was developed in MATLAB using the best available correlations. A global optimization algorithm i.e. Genetic Algorithm has been used to optimize the operating conditions. Performance of VAC is improved substantially and is shown in terms of exergy efficiency. The effect of variations of effectiveness of heat exchangers and the used of various types of heating sources will also be considered in near future.
author_names: Muhammad Umer Arshad

Muhammad Usman Ghani

Atta Ullah

Muhammad Zaman
title: Exergy Analysis and Optimization of Series and Parallel Flow Configurations of Vapor Absorption Chiller
venue: 2018 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET)
year: 2018

abstract: Abstract This paper presents the optimization of chillers operating using artificial neural networks and genetic algorithms. For the needs of generating chiller models, an artificial neural network was used, trained with data collected from an actual chiller. For that purpose the basic characteristics of artificial neural networks are shown as well as the process of making specific chiller models used for testing the results of application of the genetic algorithm in usage optimization. The optimal criteria with the shown steps for the use of the genetic algorithm and optimization results is also displayed in the paper. The results of use of artificial intelligence methods in optimization of chiller operation are verified through an actual office building model created in the simulation software EnergyPlus and through a series of experiments on an actual office building, equipped with a modern integrated BMS.
author_names: Velimir Congradac

Filip Kulic
title: Recognition of the importance of using artificial neural networks and genetic algorithms to optimize chiller operation
year: 2012

abstract: Abstract In the air cooled chiller system, sensor fault diagnosis has great significance for ensuring normal operation. However, according to the operating mechanism of the chiller system, sensors' readings exhibit dynamical data temporal dependencies and are easily affected by external factors and control parameters. To further capture the data characteristics existed in the sensors time series, in this paper, we propose a novel data temporal attention network (DAN) for the chiller sensor fault diagnosis. Based on the conventional encoder decoder network (EDN) the proposed DAN model is firstly built by adding three novel parts: the first one is a data attention mechanism embedded in the encoder, which is used to capture the dynamic data correlation between different sensors; the second part is a temporal attention mechanism, which is employed in the decoder to model the dynamic time dependencies among the sensors time series; considering the influence of external factors and control parameters, the third part is a fusion module to incorporate these influential factors from different domains. Thereafter, we design a specific chiller sensor fault diagnosis strategy using the proposed DAN model. The sensor fault diagnosis strategy uses only normal sequences for training and learns to reconstruct normal time series behaviors, and then determines the fault threshold of each chiller sensor, and finally identifies the specific sensor fault by comparing the absolute reconstruction error vector with the fault threshold vector. In the end, the experiments which adopt data sets from a real air cooled chiller platform are conducted, and detailed comparisons are made. Various magnitudes of fixed biases are introduced into eleven sensors for validation. Experimental results reveal that the sensor fault diagnosis strategy with the proposed DAN model achieves the best training and fault diagnosis performance compared with its variants and the traditional EDN model. Especially for the sensor fault diagnosis performance, comparison results demonstrate that the proposed DAN model is more sensitive to the small biases than the other contrast models and has better robustness for impacts on fault sensor in the reconstruction of the fault free sensors.
author_names: Donghui Li

Chengdong Li

Long Gao
title: A novel data temporal attention network based strategy for fault diagnosis of chiller sensors
venue: Energy and Buildings
year: 2019

abstract: The modular concept design and operation of a centrifugal chiller offers the potential of increased cycle efficiency both at full load and off load conditions compared to the single compressor chiller. Modular chiller design is that a smaller, individual chiller can be connected with another, to make a larger capacity system. In the case of a tandem chiller, there would be faced to the higher initial cost or bigger foot print than a single compressor chiller at the same capacity because manufacturing cost, weight and size will be decreased according to increase of the refrigeration capacity. Therefore, it is obvious that a modular chiller have to be improved its efficiency first by both the operation algorithm and major cycle components. Eventually, the efficiency of the modular chiller achieves up to 6% at the full load condition after added series counter flow effect as much as 3% and more 3% added by improved aerodynamic design for impeller. Moreover, maximize off design potential through the capacity combination algorithm as the way of uneven loading makes 24% improved based on AHRI 550/590.
author_names: Y J Hwang

Hanyoung Park

Saikee Oh

Horim Lee

Park Youngsu

Kam-Gyu Lee
title: The evaluation of energy saving performance for the modular design centrifugal chiller
year: 2019

abstract: Methods for chiller plant energy savings may increase component degradation, thereby decreasing reliability. Joint optimization is thus important. The problem, however, is challenging. First, existing reliability models which are functions of time in terms of years are not suitable for operation optimization. Second, efficiency optimization is static and often runs every 10 15 min given current demand while reliability change in a short period is not obvious. In this letter, a dynamic chiller reliability model and a static hybrid plant model consisting of empirical and DNN models are developed, and a weighted sum of 1 h plant power and chiller reliability is minimized with a time interval 10 min. The formulation consisting of six independent efficiency and one dynamic reliability optimization problems, and chiller power and reliability are coupled with some common variables. To address the two time scale issue, the long time scale reliability is approximated by reliability change as a result of operations using Taylor series. To efficiently solve the problem with dynamics, mixed integers, nonlinearity, and no explicit equations in DNN, a recently developed decomposition and coordination based method is combined with dynamic programming with rollout and the plant is decomposed into a simplified dynamic chiller subproblem and three simple static subproblems. Gradients needed are obtained by using finite difference without requiring explicit equations. Numerical testing demonstrates the advantages of joint optimization in terms of energy savings and reliability improvement as compared with a baseline.
author_names: Danxu Zhang

Khushboo Mittal

James Wilson

Peter B Luh

Junqiang Fan

Shalabh Gupta
title: Efficiency and Reliability Joint Optimization of Chiller Plants Based on a Hybrid Model
venue: IEEE Robotics and Automation Letters
year: 2019

abstract: Abstract Energy saving is one of the most important issues in high tech manufacturing industries, such as semiconductor and electronics, because large chilled water systems are used to satisfy big cooling load requirements. In this paper, a new optimal integrity scheme based on a two stage strategy, including a scheduling stage and an operating stage, is proposed to minimize the system energy consumption within a future time period. Instead of a lag scheme used in the general method, a forecasting scheme consisting of a series of optimal schemes at each sub time period is also proposed for the two stage design. The performance of the proposed method is examined through an industrial case. The cost of the proposed method is much less than that of the conventional method, so the proposed method is cost efficient in applications of large air conditioning systems.
author_names: Yan-yue Lu

Junghui Chen

Tzu-Chi Liu

Min-Hsien Chien
title: Using cooling load forecast as the optimal operation scheme for a large multi chiller system
year: 2011

abstract: In semiconductor factories and electronics factories, a large multi chiller system is needed to satisfy strict cooling load requirements. In order to save energy, it is worthwhile to design the chilled water system operation. In this paper, a design methodology is developed for multi chiller system in which chiller units consist of different cooling capacities chiller that run in parallel. The optimal design problem can be expressed as a mixed integer nonlinear programming (MINLP) which minimizes the system energy consumption within a future time period. In order to solve this complex problem, the two stage strategy is employed to divide the problem into a scheduling stage and an operating stage. Instead of a lag scheme obtained with general method, this forecasting scheme consists of a series of optimal scheme at each sub time period and can offer operating reference for operator. The effectiveness of the proposed method is illustrated by an industrial case
author_names: Yan Yue Lu

An ping Liao
title: Study on Operation and Control of Large Multi Chiller System
year: 2011

abstract: Centrifugal chiller plants (CCP) are widely used in air conditioning systems, its operation optimization can save lots of energy and has great significance in environmental protection. The optimization is a large scale nonlinear problem and there is no practical algorithm until now. This paper proposes a new method to do this operation optimization using continuous piecewise linear programming (CPWLP) The main idea is transforming the nonlinear problem into a series of linear programmings by approximating the original system using piecewise linear representation. For CPWLP, some properties are discussed and an algorithm is given. Using CPWLP, CCP system is optimized and its energy performance is improved significantly.
author_names: X Huang

Jun Xu

Shuning Wang
title: Operation optimization for centrifugal chiller plants using continuous piecewise linear programming
venue: 2010 IEEE International Conference on Systems, Man and Cybernetics
year: 2010

abstract: With the advent of advanced metering infrastructure and data analytics, there is an increasing focus of nonintrusive load monitoring application (NILM) towards residential buildings and industrial systems. In this paper, a data driven approach for NILM analysis is implemented using an actual chiller plant system operation dataset as the case study. The opportunities and challenges of NILM application from the dataset collection perspective are then briefly discussed. Unlike NILM residential buildings applications for on off equipment status determination, NILM for industrial system focuses on estimating the power level of the disaggregated components.
author_names: Ding Hong Yuan

Benjamin Si Hao Chew

Yan Xu
title: A Time Series Data Feature Based Back Propagation Neural Network Approach for Non intrusive Load Monitoring
venue: 2019 9th International Conference on Power and Energy Systems (ICPES)
year: 2019