A. Takači, T. Žikić‐Došenović and Z. Zavargó
The purpose of this paper is to introduce a mathematical model, an algorithm and numerical procedure for determining the duration of a diafiltration process with variable volume…
Abstract
Purpose
The purpose of this paper is to introduce a mathematical model, an algorithm and numerical procedure for determining the duration of a diafiltration process with variable volume. This model should decrease diafiltration time and water usage.
Design/methodology/approach
A mathematical model of a diafiltration process, with constant water adding is considered, as well as the relationships between macro‐ and micro‐solute and the equation for process duration. By introducing α (t)=QD/QF, i.e. time dependent ratio of diafiltration water flow and filtrate flow rate, a mathematical model of diafiltration process with time dependent water adding is proposed. In order to solve this model, an algorithm is suggested, based on the simulation of single time dependent water adding process with a sequence of constant water adding processes. The algorithm is applied for developing both a numerical procedure and simple QBASIC‐program that are tested on one illustrative example.
Findings
The results obtained by the algorithm improve the diafiltration process time around 10 percent and are a step towards finding an optimal dependence function α (t).
Research limitations/implications
Further on, the analysis of water usage in variable volume diafiltration needs to be done. Also, the problem of finding the optimal α (t) is still open.
Practical implications
The suggested algorithm is applicable to various membrane filtration processes and can be applied with little modification of the existing filtration equitment.
Originality/value
This paper is the first to view ALFA as a continuous function of time. Previous authors have considered step functions but never used this general approach.
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Oluwafemi Ajayi and Reolyn Heymann
Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly…
Abstract
Purpose
Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly increasing global demand for cloud services and other technological services. This projected sectoral growth is expected to translate into increased energy demand from the sector, which is already considered a major energy consumer unless innovative steps are used to drive effective energy management systems. The purpose of this study is to provide insights into the expected energy demand of the DC and the impact each measured parameter has on the building's energy demand profile. This serves as a basis for the design of an effective energy management system.
Design/methodology/approach
This study proposes novel tunicate swarm algorithm (TSA) for training an artificial neural network model used for predicting the energy demand of a DC. The objective is to find the optimal weights and biases of the model while avoiding commonly faced challenges when using the backpropagation algorithm. The model implementation is based on historical energy consumption data of an anonymous DC operator in Cape Town, South Africa. The data set provided consists of variables such as ambient temperature, ambient relative humidity, chiller output temperature and computer room air conditioning air supply temperature, which serve as inputs to the neural network that is designed to predict the DC’s hourly energy consumption for July 2020. Upon preprocessing of the data set, total sample number for each represented variable was 464. The 80:20 splitting ratio was used to divide the data set into training and testing set respectively, making 452 samples for the training set and 112 samples for the testing set. A weights-based approach has also been used to analyze the relative impact of the model’s input parameters on the DC’s energy demand pattern.
Findings
The performance of the proposed model has been compared with those of neural network models trained using state of the art algorithms such as moth flame optimization, whale optimization algorithm and ant lion optimizer. From analysis, it was found that the proposed TSA outperformed the other methods in training the model based on their mean squared error, root mean squared error, mean absolute error, mean absolute percentage error and prediction accuracy. Analyzing the relative percentage contribution of the model's input parameters based on the weights of the neural network also shows that the ambient temperature of the DC has the highest impact on the building’s energy demand pattern.
Research limitations/implications
The proposed novel model can be applied to solving other complex engineering problems such as regression and classification. The methodology for optimizing the multi-layered perceptron neural network can also be further applied to other forms of neural networks for improved performance.
Practical implications
Based on the forecasted energy demand of the DC and an understanding of how the input parameters impact the building's energy demand pattern, neural networks can be deployed to optimize the cooling systems of the DC for reduced energy cost.
Originality/value
The use of TSA for optimizing the weights and biases of a neural network is a novel study. The application context of this study which is DCs is quite untapped in the literature, leaving many gaps for further research. The proposed prediction model can be further applied to other regression tasks and classification tasks. Another contribution of this study is the analysis of the neural network's input parameters, which provides insight into the level to which each parameter influences the DC’s energy demand profile.