IoT-based waste management: hybrid optimal routing and waste classification model.

Internet of Things (IoT) makes connectivity between physical devices which are embedded with sensors, software, and connectivity that let them to communicate and transfer data. This technology makes it possible to collect and transfer data from a vast network device, opening the door to the development of automatic and more efficiency systems. The term "waste management" refers to all of the responsibilities essential to regulate trash, from the point of gathering through reusing and monitoring. Reducing the hazardous consequences of such garbage on the environment and human health is the goal of waste management. By considering these hazardous consequences, this research work is interested in working on an efficient waste management system. The utilization of IoT devices enables municipalities to optimize waste management operations by leveraging data insights. This information aids in scheduling waste collections more effectively and planning optimal routes. Therefore, the research work proposes an IoT-based waste management system with two vital processes such as IoT routing and waste management. At first, routing in IoT is done by proposing hybrid optimization algorithm named Snake Optimization Updated Beluga Whale Optimization algorithm (SOUBWO) under constraints such as distance, energy, link quality, delay, and trust. Secondly, waste management is worked on by following steps including pre-processing, segmentation, feature extraction, and classification. The waste images collected by IoT devices are transmitted from source node (SN) to destination node (DN) by optimal routing. Those transmitted waste images are pre-processed by Wiener filtering process. Consequently, the pre-processed images are segmented by employing proposed Balanced Iterative Reducing and Clustering Using Hierarchies-Altered Distance Metrics (BIRCH-ADM) algorithm. Subsequently, features such as multi-text on histogram feature, proposed Local Gabor XOR Pattern (LGXP)-based feature using novel image processing techniques, and statistical features are extracted. Finally, these extracted features are efficiently classified by hybrid classification model which is formed by integrating conventional deep maxout and Bidirectional-Long Short Term Memory (Bi-LSTM) networks. The effectiveness of the proposed approach is validated through various analyses, including performance and statistical analyses. Moreover, the proposed scheme demonstrates minimal energy consumption, with a recorded value of 0.123. In contrast, conventional methodologies exhibit higher energy consumption, with values such as SOA = 0.237, BWO = 0.146, BES = 0.183, SMO = 0.158, CHOA = 0.174, and PSO = 0.189, respectively. By this hybrid classification model, the process of classification on waste is effectively done and moreover its effectiveness is proved by various analyses.

Assessing the bioactive potential of low-cost textile dyes extracted from brown seaweeds and their dyeing properties.

This study focuses on the extraction and dyeing properties of natural fabric dyes derived from brown seaweeds, namely Padina tetrastromatica, Sargassum tenerrimum, and Turbinaria ornata. Various solvents (acetone, ethanol, methanol, and water) and mordants (CH3 COOH, FeSO4, and NaHCO3) were used to extract the dyes and achieve different shades with excellent fastness properties. Phytochemical and FTIR analyses were performed to identify the phytochemicals responsible for dyeing. The dyed cotton fabrics exhibited a range of colors based on the mordants and solvents used. Fastness assessments revealed that aqueous and ethanol dye extracts exhibited superior properties compared to acetone and methanol extracts. The influence of mordants on cotton fibers' fastness properties was also evaluated. In addition to the above findings, this study makes a significant contribution to the field by exploring the bioactive potential of natural fabric dyes derived from brown seaweeds. The utilization of these abundant and low-cost seaweed sources for dye extraction provides a sustainable alternative to synthetic dyes, addressing environmental concerns associated with the textile industry. Furthermore, the comprehensive analysis of different solvents and mordants in obtaining various shades and excellent fastness properties enhances our understanding of the dyeing process and opens avenues for further research in the development of eco-friendly textile dyes.

Optimization of engine parameters using NSGA II for the comprehensive reduction of emissions from VCR engine fuelled with ROME biodiesel.

Diesel engines are most widely used as power plant for many applications, like automotive, agricultural purposes, portable machines and remote location power generation, because of their higher torque, power output, energy content per unit mass and cost of fuel. Because of the higher compression ratios, the diesel engines are able to produce greater cylinder pressures resulting in higher temperatures and thermal efficiency. On other hand, the diesel engines produce COx, NOx, Soot and sulphur emissions which are harmful and these pollute the environment leading to acid rain, global warming and variety of human diseases. Also, the present emission regulations are framed such a way to ensure the environmental sustainability in addition to the economic and social importance. These constraints make the researchers find an alternate fuel for replacing the diesel fuel on the existing diesel engines for the reduction of environmental pollutions. Biodiesel is found to be a very good alternative fuel obtained from natural resources and having good energy with least possible emissions. Rubber seed methyl ester (ROME) is one kind of the biofuel can be used in the existing diesel without any engine modifications. The ROME is produced using trans esterification process and the biodiesel blends are prepared in the sequence of B20, B40, B60 and B80. The ROME is tested on the Variable Compression Ratio (VCR) engine to test the emission characteristic in line with the performance characteristics. To reduce the emissions, the prediction models are developed for CO and NOx using the Response Surface Methodology (RSM). The models are verified through the ANOVA and p-test for their adequacy to create the hypothesis of the experimentation. The NSGA II evolutionary multi-objective optimization is used to optimise the engine parameters to minimise the pollutions from the ROME fuelled engine. The optimized parameters are tested and verified experimentally for the least possible emissions.

Experimental investigation on the performance characteristics and emissions of a CI engine fueled with enhanced microwave-assisted Karanja seed bio-oil.

The main objective of the present research work is to utilise the produced bio-oil from microwave pyrolysis of Karanja, a non-edible seed, as fuel for diesel engines by increasing some up-gradation in the quality of the fuel. The emulsification process is carried out to improve the stability of the diesel-bio-oil blend using SPAN 80 and TWEEN 80, which lasted for 28 days without any layer separation termed as EKB20. The addition of 5% DEE and 10% DEE into EKB20 is done to enhance the combustion characteristics of the diesel engine. The produced bio-oil fuels were tested in a Kirloskar make, four-stroke, single-cylinder, direct injection diesel engine of 5.2 kW rated power output. The addition of DEE reduces the peak pressure by 4 bar and increases the heat release rate due to the higher volatility of DEE. At full load conditions, the thermal brake efficiency improved by 9.31% and 14.11%, respectively, compared to EKB20. Adding 5% DEE and 10% DEE at the rated power output reduced the smoke density by 18.42% and 60.25%, respectively, compared to EKB20 and 5% and 4% compared to diesel. The addition of 5% DEE and 10% DEE shows a 39% and 51% increase in NOX concentration and a 90% reduction in CO emission at the maximum brake power output. Hence, it is concluded that the fuels EKB20 + 5% DEE and EKB20 + 10% DEE can be used as alternative fuels for diesel engines.

Modelling and evaluation of combustion emission characteristics of COME biodiesel using RSM and ANN-a lead for pollution reduction.

Nowadays, the emissions from the diesel engines are focused lot to minimise the environmental pollutions in accordance with the emission standards. In this regard, biodiesels are found to be efficient for the diesel engines due to their higher energy contents and low exhaust emissions. Use of biofuel in association with diesel will be an efficient way for the cost-effective performance of the diesel engines with reduced pollutions. The COME is an efficient combustible oil, in which the diesel is blended at different proportions to identify their suitability in the diesel engines. In this regard, the properties of the COME-Diesel blends are determined and analysed for their influence on the combustion characteristics. To understand the performance and emission characteristics of blends, experiments are carried out on the variable compression ratio (VCR) engine keeping the blend, compression ratio, load, and speed as variables. The response surface methodology (RSM) used as a tool for designing and conducting the experiments according to the predetermined variables. The experimental sets generated are performed to determine the NO and HC emissions (response functions). The adequacy of the models is verified through ANOVA and through the p and F tests. The experimental design matrix is also used in training the artificial neural network (ANN) to develop the empirical models. The models from RSM and ANN are experimented and the results obtained from both the models are compared for their accuracy levels. Once the hypothesis is developed for the biodiesel and engine setup, the emission models will be used for the optimising the engine operating parameters and blends to minimise the pollutions from engine for wide operating conditions.