Feature Data-driven Reinforced Fuzzy Radial Basis Function Neural Network Classifier With the Aid of Preprocessing Techniques and Particle Swarm Optimization

In this study, reinforced fuzzy radial basis function neural networks (FRBFNN) classifier driven by feature extracted data completed with the aid of effectively preprocessing techniques and evolutionary optimization, and its comprehensive design methodology are introduced. An Overall structure of the reinforced FRBFNN comprises the preprocessing part, the premise part and the consequence part of fuzzy rules of the network. In the preprocessing part, four types of preprocessing algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), combination of PCA and LDA (Hybrid PCA) and fuzzy transform (FT) are considered. To extract feature data suitable to characterize signal data, the feature extraction of information data is carried out through the dimensionality reduction done by the preprocessing technique, and then the reduced data are used as the input to the FRBFNN classifier. In the premise part of fuzzy rules of the network, the number of fuzzy rules is determined according to the number of clusters by fuzzy c-means (FCM) clustering. The fitness values of individual fuzzy rules are obtained based on data distribution. In the consequence part of fuzzy rules of the network, the parameters of connection weights located between the hidden layer and the output layer of FRBFNN classifier are estimated by means of the least square estimation (LSE). Particle swarm optimization (PSO) is exploited for structural as well as parametric optimization in the FRBFNN classifier. The parameters to be optimized by PSO are related to six factors such as the determination of whether to use data preprocessing, the type of data preprocessing technique, the number of input variables reduced by the preprocessing technique, fuzzification coefficient (FC) and the number of fuzzy rules used in fuzzy c-means (FCM) clustering, and the type of connection weights. By using diverse benchmark dataset obtained from UCI repository, the classification performance of the reinforced FRBFNN classifier was evaluated. Through a variety of classification algorithms existed in the Weka data mining software (Weka), the classification performance of the reinforced FRBFNN classifier was compared as well. The superiority of the proposed classifier is demonstrated through Friedman test. Furthermore, we assessed the classification performance of the reinforced FRBFNN classifier applied to black plastic wastes spectral data acquired from Raman and Laser induced breakdown spectroscopy (LIBS) equipment for the practical application of the material sorting system of the black plastic wastes.

Production and Optimization of Energy Rich Biofuel through Co-Pyrolysis by Utilizing Mixed Agricultural Residues and Mixed Waste Plastics

Two totally waste products, agricultural residues and mixed plastic wastes collected from domestic and industrial sectors, are used in this study for the recovery of energy rich biofuel and value-added chemicals. The copyrolysis experiments using fixed bed reactor are conducted in order to analyse the synergetic effects. The experimental works are carried out with different proportion of mixed plastics blended with agricultural residues. The reaction temperature and biomass-to-waste plastics ratio on product distributions are studied and addressed. The thermogravimetric analysis conducted at different temperatures clearly distinguished the pyrolysis behaviours of biomass and plastics. The positive synergistic effects defined as higher yield of volatiles compared to predicted yield for bio-oil were identified at particular mixing ratio. Both biomass wastes and plastic wastes show optimal performance of 60.42 wt% oil yield at 60% addition of waste plastics. The oil products obtained under favourable conditions have a higher heating value compared to the oil obtained from biomass pyrolysis. The GC-MS study confirmed that the interaction between biomass and plastics during copyrolysis resulted in decreased oxygenated contents in the oil products.

Mechanical Properties of Cement Composites Using Modified Plastics by Gamma Irradiation

Recently, pollution caused by an increasing amount of worldwide plastic waste has become a global problem. However, these concerns can be alleviated by the use of gamma-ray technology. Using radiation technology, plastic wastes can be converted into a variety of useful purposes presenting powerful opportunities for environmental sustainability and material innovations. Plastics are strong, durable, waterproof, lightweight, easy to mold, and recyclable. In this study, plastic aggregate modified by gamma irradiation was mixed into cement composites, and mechanical property evaluation experiments were conducted. As a result, it was confirmed that the physical performance of cement composites was improved by up to 70% in the case of using the modified plastic aggregates compared to the general plastic aggregate.

Transforming Plastic Waste into Porous Carbon for Capturing Carbon Dioxide: A Review

Plastic waste generation has increased dramatically every day. Indiscriminate disposal of plastic wastes can lead to several negative impacts on the environment, such as a significant increase in greenhouse gas emissions and water pollution. Therefore, it is wise to think of other alternatives to reduce plastic wastes without affecting the environment, including converting them into valuable products using effective methods such as pyrolysis. Products from the pyrolysis process encompassing of liquid, gas, and solid residues (char) can be turned into beneficial products, as the liquid product can be used as a commercial fuel and char can function as an excellent adsorbent. The char produced from plastic wastes could be modified to enhance carbon dioxide (CO2) adsorption performance. Therefore, this review attempts to compile relevant knowledge on the potential of adsorbents derived from waste plastic to capture CO2. This review was performed in accordance with PRISMA guidelines. The plastic-waste-derived activated carbon, as an adsorbent, could provide a promising method to solve the two environmental issues (CO2 emission and solid management) simultaneously. In addition, the future perspective on char derived from waste plastics is highlighted.

Fuzzy Based Spatial Risk Evaluation of Plastic Pollution: A Case Study of Anambra State of Nigeria

Natural systems, human health, and artistic sensitivities are all threatened by plastic pollution in most developed and developing countries. Plastic has emerged as a major global threat with rivers serving as sink for transported plastics, emanating from the terrestrial environment as a result of human activities. Anambra State in Nigeria is arguably the business hub of the South-eastern part of Nigeria, with a massive output of plastic wastes daily from individuals, commercial activities and industries. Owing to an inefficient waste management system, plastic leakage into her drainage networks is a critical environmental challenge. The aim of this study is to geospatially model the vulnerability associated with the various plastic leakage factors to the environment. To achieve this aim, data on different thematic variables which include plastic waste density, slope, land-use, drainage density and distance to drainage network of the study area were modelled, Geographic Information Systems (GIS) was used to delineate the variables in order to obtain final risk map for the study area. The result of the study indicates that a total area very high risk is 1840.03 km2, this constitutes about 40.11% of the study area. Local Governments Areas (LGA) located in the southern part of the study area is more susceptible to plastic waste leakage, this could be linked to factors like high dense population and increasing rate of urbanization in the region. It is recommended that waste collection should be frequent, strategic and higher priority should be attached to the high risked area from this study. Anambra State Government also needs to work together with plastic recycling companies, for effective collection of plastic wastes in the areas classified as hotspots in plastic litter accumulation as one of the mitigation measures.