Chemical and biological reactions of solidification of peat using ordinary portland cement (OPC) and coal ashes

Construction over peat area have often posed a challenge to geotechnical engineers. After decades of study on peat stabilisation techniques, there are still no absolute formulation or guideline that have been established to handle this issue. Some researchers have proposed solidification of peat but a few researchers have also discovered that solidified peat seemed to decrease its strength after a certain period of time. Therefore, understanding the chemical and biological reaction behind the peat solidification is vital to understand the limitation of this treatment technique. In this study, all three types of peat; fabric, hemic and sapric were mixed using Mixing 1 and Mixing 2 formulation which consisted of ordinary Portland cement, fly ash and bottom ash at various ratio. The mixtures of peat-binder-filler were subjected to the unconfined compressive strength (UCS) test, bacterial count test and chemical elemental analysis by using XRF, XRD, FTIR and EDS. Two pattern of strength over curing period were observed. Mixing 1 samples showed a steadily increase in strength over curing period until Day 56 while Mixing 2 showed a decrease in strength pattern at Day 28 and Day 56. Samples which increase in strength steadily have less bacterial count and enzymatic activity with increase quantity of crystallites. Samples with lower strength recorded increase in bacterial count and enzymatic activity with less crystallites. Analysis using XRD showed that pargasite (NaCa2[Mg4Al](Si6Al2)O22(OH)2) was formed in the higher strength samples while in the lower strength samples, pargasite was predicted to be converted into monosodium phosphate and Mg(OH)2 as bacterial consortium was re-activated. The Michaelis�Menten coefficient, Km of the bio-chemical reaction in solidified peat was calculated as 303.60. This showed that reaction which happened during solidification work was inefficient. The kinetics for crystallite formation with enzymatic effect is modelled as 135.42 (1/[S] + 0.44605) which means, when pargasite formed is lower, the amount of enzyme secretes is higher

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area

Chaotic Time Series Forecasting Using Higher Order Neural Networks

This study presents a novel application and comparison of higher order neural networks (HONNs) to forecast benchmark chaotic time series. Two models of HONNs were implemented, namely functional link neural network (FLNN) and pi-sigma neural network (PSNN). These models were tested on two benchmark time series; the monthly smoothed sunspot numbers and the Mackey-Glass time-delay differential equation time series. The forecasting performance of the HONNs is compared against the performance of different models previously used in the literature such as fuzzy and neural networks models. Simulation results showed that FLNN and PSNN offer good performance compared to many previously used hybrid models

An ensemble CRT, RVFLN, SVM method for estimating Propane Spot Price

In this paper, we propose an ensemble of the CRT-RVFLN-SVM (Classification and Regression Tree (CRT), Random Variable Functional Link Neural Network (RVFLN), and Support Vector Machine (SVM)) to improve robustness and effectiveness in estimating propane spot price. The propane spot price data which are collected from the Energy Information Administration of the US Department of Energy and Barchart were used to build an ensemble CRT-RVFLN-SVM model for the estimating of propane spot price. For the purpose of evaluation, the constituted intelligent computing technologies of the proposed ensemble methodology in addition to Multilayer Back-Propagation Neural Network (MBPNN) were also applied to estimate the propane spot price. Experimental results show that the proposed ensemble CRT-RVFLN-SVM model has improved the performance of CRT, RVFLN, SVM, and MBPNN. The can help to reduce the level of future uncertainty of the propane spot price. Propane investors can use our model as an alternative investment tool for generating more revenue because accurate estimations of future propane price implies generating more profit

Driver drowsiness detection using Gray Wolf Optimizer based on voice recognition

Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respective

Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition

Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respectively