The industrial sector is the largest user of energy in Malaysia. Industrial motors account for a major segment of total industrial energy use. Since motors are the principle energy users, different energy savings strategies have been applied to reduce their energy consumption and associated emissions released into the atmosphere. These strategies include using highly efficient motors, variable speed drive (VSD), and capacitor banks to improve the power factor. It has been estimated that there can be a total energy savings of 1765, 2703 and 3605Â MWh by utilizing energy-efficient motors for 50%, 75% and 100% loads, respectively. It was also found that for different motor loads, an estimated US$115,936 US$173,019 and US$230,693 can be saved in anticipated energy costs. Similarly, it is hypothesized that a significant amount of energy can be saved using VSD and capacitor banks to reduce speed and improve the power factor, thus cutting energy costs. Moreover, a substantial reduction in the amount of emissions can be effected together with the associated energy savings for different energy savings strategies. In addition, the payback period for different energy savings strategies has been found to be reasonable in some cases.

Energy and emission analysis for industrial motors in MalaysiaMalaysia

Energy storage is one of the core concepts demonstrated incredibly remarkable effectiveness in various energy systems. Energy storage systems are vital for maximizing the available energy sources, thus lowering energy consumption and costs, reducing environmental impacts, and enhancing the power grids' flexibility and reliability. Artificial intelligence (AI) progressively plays a pivotal role in designing and optimizing thermal energy storage systems (TESS). Recently, plenty of studies have been conducted to examine the feasibility of applying artificial intelligence techniques, such as particle swarm optimization (PSO), artificial neural networks (ANN), square vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), in the energy storage sector. This study introduces the classifications, roles, and efficient design optimization of energy systems in various applications using different artificial intelligence approaches. This study discusses the progress made regarding implementing artificial intelligence and its sub-categories for optimizing, predicting, and controlling the performance of energy systems that contain thermal energy storage facilities. In addition, the performance of these technologies is thoroughly analyzed, highlighting their noticeable accuracy while carrying out different objectives. Recommendations and future research points are introduced to offer new concepts and inspiration for the application of AI in TESS. 

Application of artificial intelligence for prediction, optimization, and control of thermal energy storage systems

The rapid evolution of the Internet and communication technologies has fueled the proliferation of wireless sensor network (WSN) technology, which is increasingly important in today's interconnected world. For a broad variety of industries and applications, an enormous number of sensing devices continuously create and/or gather copious amounts of sensory data. However, it has been shown that WSN is susceptible to security flaws. These networks' abrasive and unmanaged deployment, along with their limited resources and the amount of data produced, raise serious security issues. The development of trustworthy solutions that include quick and continuous processes for live data stream analysis allowing the identification of flooding assaults is crucial since WSN applications are of the utmost importance. To put it in plain words: The assault is carried out by repeatedly sending pointless requests to the target computer in an effort to overwhelm it, cause the systems to fail, and prevent people from accessing the network or machine. 

Dynamic Intelligence-Driven Engineering Flooding Attack Prediction Using Ensemble Learning

Machine Learning Methods for Diagnosis of Eye-Related Diseases: A Systematic Review Study Based on Ophthalmic Imaging Modalities

Energy utilization throughout the world is expanding at an alarming rate. This has just demonstrated its effect on the consumption of fuel resources and natural concerns (a drastic atmospheric deviation and debilitating of the O 3 layer). Positively, this heightening propensity of energy inadequacy will get disturbed later on. Effectiveness upgrade activities are viewed as the necessary arrangement in decreasing energy usage and ultimately opposing the worldwide natural effects of the all-out world energy produced, ventilation system, particularly the axial fans, burn-through about 20%. Hence, the essential focus on worldwide energy policy-makers is to upgrade energy effectiveness in the ventilation system. According to the studies, striking energy investment funds can be refined by controlling the ventilation system speed utilizing VFDs. Therefore, studies and investigations center principally around VFD control strategies to improve the ventilation system effectiveness. This article gave state-of-the-artwork on the ventilation system and VSD that reduces the ventilation system’s overall energy consumption. Various energy efficiency strategies, selection of various components, and intelligent flow prediction techniques are represented in this article. Finally, the prospect encounters of AI-based models employed in the environmental area are discussed and proposed.

A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence

This research article corroborates the working of a model reference adaptive model (MRAS) with fractional-order proportional-integral (FOPI
 $$^\lambda $$
 )-based encoderless speed control approach for ventilation system drive using recurrent neural network (RNN) for the low- and medium-range operation. 
The purpose of this study is to minimize the energy loss due to fluctuations and variation in the rotor speed and also find the optimum values of FOPI
 $$^\lambda $$
 by using a recurrent neural network to enhance the overall implementation of the system.
 In this perspective, the low-speed execution of MRAS is poor due to the existence of a pure integral and derivative parameter. Towards enhancement of the performance at speed region, a MRAS method with RNN is used. The network is trained using the Levenberg–Marquardt (LM) algorithm, and FOPI
 $$^\lambda $$
 control method is used for tuning the gain of proportional-integral of speed and current controller of the encoderless speed control of the ventilation drive.
 The presented RNN speed estimator with FOPI
 $$^\lambda $$
 controller has shown better performance and stability in transitory and stable operation as well as it also provides an enhancement in the overall efficiency of the ventilation drive.
 The validation of the presented algorithm is detailed experiments on a fully digitized 5.5 kW ventilation system using the Lab VIEW interface.

Development of energy efficient drive for ventilation system using recurrent neural network

Poor airflow ventilation systems fetch a progressively critical challenge for many working areas, which transmits many calamitous physical consequences on operatives’ health and quality of work. However, accurate monitoring and prediction of the ventilation systems airflow remain challenging due to the multiple properties and non-linear characteristics in time and space. Machine learning and deep neural network techniques have recently received significant consideration for their real-world applications in numerous areas. This affluence key feature is a deep neural network motivated by the data handling in biological brains. In this article, we applied one of the representative deep neural network techniques, i.e., 1D-CNN with LSTM, to predict the variation in the airflow of the ventilation system. These utilize CNN advantage, which effectively extracts the systems feature, whereas the LSTM can imitate the long-term sequential progression of input time-series data. It provides SHAP analysis that can be used to understand the output of the proposed model to forecast the airflow of the ventilation system. This method computes an approximation of the influence of individual features for predicting the non-linear element. Subsequently, five models, i.e., CNN, LSTM, 1D-CNN-LSTM, ANN, and LR, are used to predict the ventilation system’s airflow. The result shows that the proposed model 1D-CNN-LSTM accuracy and loss are 96.7 %, and 0.01348 provides the finest result compared to others. The aim of this research lies in the application of a complex model to interpret the airflow of the ventilation system. It is of great interest as it consents us to comprehend how a model behaves and enables us to take pre-emptive methods to improve working efficiency. 

Convolutional neural network-long short term memory optimization for accurate prediction of airflow in a ventilation system

An adaptive neural fuzzy interface structure optimisation for prediction of energy consumption and airflow of a ventilation system

Heavy duty electrical drives employ slip ring induction motors (SRIMs) owing to their excellent starting and running performance characteristics. However, hazardous working environment prevalent in certain industries renders these SRIMs prone to a number of unwanted anomalies. Therefore, condition monitoring of the working behavior of SRIMs is indispensable for accomplishing substantial production with minimum downtime. In this regard, the present research work proposes an efficient and effective condition monitoring scheme for the detection of ball bearing damage, which is a frequent fault in a SRIM. This is done using stator current as a viable detection parameter. Fast Fourier Transform (FFT) and discrete wavelet transform (DWT) is employed for the design and realization of the proposed scheme along with state variable band-pass filters. This forms the three-tier approach for fault detection. Furthermore, artificial neural network (ANN) based pattern recognition is used as a fourth-tier confirmation of the presence or absence of bearing damages as a process of fault pin-pointing. Real-time validation of the aforementioned scheme is carried out in LabVIEW based laboratory interface. Hazardous working environment in certain industries renders the use of sophisticated equipment and machinery to be relatively unviable. Therefore, a simplistic approach is rather indispensible in the current scenario. Therein lays the novelty of the present research work. The entire design, analysis and further testing is carried out in MATLAB/ Simulink and LabVIEW based laboratory interface using a 5.5 kW, 3-phase, 415 V, 50 Hz SRIM.

ANN Based Fault Detection Scheme for Bearing Condition Monitoring in SRIMs using FFT, DWT and Band-pass Filters

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