Search results for: waste classification

Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, Lionel L. J. Ang, Algernon C. S. Hong, Danette S. E. Tan, Grace H. B. Foo, K. Q. Hong, L. M. Cheng, M. L. Leong

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This paper presents a novel rapid soil classification technique that combines computer vision with four-probe soil electrical resistivity method and cone penetration test (CPT), to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from local construction projects are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labour-intensive. Thus, a rapid classification method is needed at the SGs. Computer vision, four-probe soil electrical resistivity and CPT were combined into an innovative non-destructive and instantaneous classification method for this purpose. The computer vision technique comprises soil image acquisition using industrial grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). Complementing the computer vision technique, the apparent electrical resistivity of soil (ρ) is measured using a set of four probes arranged in Wenner’s array. It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the soil strength is measured using a modified mini cone penetrometer, and w is measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay” and an even mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay”. It is also found that these parameters can be integrated with the computer vision technique on-site to complete the rapid soil classification in less than three minutes.

Keywords: Computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

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Authors: Mohammed A. Elhaj, Jamal S. Yassin

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In the present time, energy crises are considered a severe problem across the world. For the protection of global environment and maintain ecological balance, energy saving is considered one of the most vital issues from the view point of fuel consumption. As the industrial sectors everywhere continue efforts to improve their energy efficiency, recovering waste heat losses provides an attractive opportunity for an emission free and less costly energy resource. In the other hand the using of solar energy has become more insistent particularly after the high gross of prices and running off the conventional energy sources. Therefore, it is essential that we should endeavor for waste heat recovery as well as solar energy by making significant and concrete efforts. For these reasons this investigation is carried out to study and analyze the performance of a power plant working by a combined cycle in which Heat Recovery System Generator (HRSG) gets its energy from the waste heat of a gas turbine unit. Evaluation of the performance of the plant is based on different thermal efficiencies of the main components in addition to the second law analysis considering the exergy destructions for the whole components. The contribution factors including the solar as well as the wasted energy are considered in the calculations. The final results have shown that there is significant exergy destruction in solar concentrator and the combustion chamber of the gas turbine unit. Other components such as compressor, gas turbine, steam turbine and heat exchangers having insignificant exergy destruction. Also, solar energy can contribute by about 27% of the input energy to the plant while the energy lost with exhaust gases can contribute by about 64% at maximum cases.

Keywords: solar energy, environment, efficiency, waste heat, steam generator, performance, exergy destruction

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Authors: Inas Kamel, Noor Z. Habib

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Utilizing construction and demolition wastes in hotmix asphalt (HMA) pavement construction can reduce the adverse environmental effect of its inadequate disposal and reduce the pressure of extracting and processing mineral aggregates (MA). This study aims to examine the viability of replacing MA by recycled construction and demolition waste aggregates (RCDWA) in the wearing course of asphaltic concrete (AC) pavements without compromising its loadbearing capacity. The Marshall Method was used to evaluate the performance of AC wearing course specimens by replacing MA by 10%, 20% and 30% RCDWA. Grade 60/70 bitumen was used in the range 3.0-5.5%, with 05% increments, to generate the optimum bitumen content (OBC). From the volumetric analysis and test property curves, the mixture containing 20% RCDWA was chosen as the preferred mix at 5.1% OBC. It possessed a 10% increase in Marshall Stability compared to the reference specimen, containing 100% MA, and a 6% increase in Marshall flow.

Keywords: aggregate, asphaltic concrete, Marshall method, optimum bitumen content, recycled construction and demolition waste

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Mezhoud Samy, Toumi Youcef, Boukendekdji Otmane

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Nowadays, industrial by-products and waste are increasing along with public needs increase. The engineering sector has turned to sustainable development by emphasizing the aspects of environmental and life cycle assessment as an important objective. Among this waste, the remains of the red bricks (DBR) may be an alternative worth checking out, given their availability and abundance at the construction sites. In this context, this work aims to valorize DBR in the concrete road (BR). The incorporation of DBR is carried out by the substitution of the granular fractions of mixtures from noble quarry materials. The experimental plan aims to determine the physico-mechanical performance and environmental performance of manufactured BRs from DBR with a cement content (6.5%) and compared with a control BR without DBR. The studied characteristics are proctor, resistance to compression, resistance to flexural tensile at 7 and 28 days, modulus of elasticity, and total shrinkage. The results of this experimental study showed that the characteristics of recycled aggregates (DBR) are lower than those of natural aggregates but remain acceptable with respect to regulations. Results demonstrate the mechanical performance of BR made from less DBR than the control BR without DBR but remains appreciable and encourage their jobs in the road sector. Recycled aggregates can constitute an interesting economic and ecological alternative but require elementary precautions before any use.

Keywords: life cycle assessment, brick waste, road concrete, performance

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Authors: Ahmed El-Banbi

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Several PVT models exist to represent how PVT properties are handled in sub-surface and surface engineering calculations for oil and gas production. The most commonly used models include black oil, modified black oil (MBO), and compositional models. These models are used in calculations that allow engineers to optimize and forecast well and reservoir performance (e.g., reservoir simulation calculations, material balance, nodal analysis, surface facilities, etc.). The choice of which model is dependent on fluid type and the production process (e.g., depletion, water injection, gas injection, etc.). Based on close to 2,000 reservoir fluid samples collected from different basins and locations, this paper presents some conclusions on the occurrence of reservoir fluids. It also reviews the common methods used to classify reservoir fluid types. Based on new criteria related to the production behavior of different fluids and economic considerations, an updated classification of reservoir fluid types is presented in the paper. Recommendations on the use of different PVT models to simulate the behavior of different reservoir fluid types are discussed. Each PVT model requirement is highlighted. Available methods for the calculation of PVT properties from each model are also discussed. Practical recommendations and tips on how to control the calculations to achieve the most accurate results are given.

Keywords: PVT models, fluid types, PVT properties, fluids classification

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Authors: Asma A. Alsufyani, Maram A. Alharthi, Maha J. Althobaiti, Manal S. Alharthi, Huda Rizq

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Twitter is one of the most popular microblogging sites that allows users to publish short text messages called 'tweets'. Increasing the number of accounts to follow (followings) increases the number of tweets that will be displayed from different topics in an unclassified manner in the timeline of the user. Therefore, it can be a vital solution for many Twitter users to have their tweets in a timeline classified into general categories to save the user’s time and to provide easy and quick access to tweets based on topics. In this paper, we developed a classifier for timeline tweets trained on a dataset consisting of 3600 tweets in total, which were collected from Saudi Twitter and annotated manually. We experimented with the well-known Bag-of-Words approach to text classification, and we used support vector machines (SVM) in the training process. The trained classifier performed well on a test dataset, with an average F1-measure equal to 92.3%. The classifier has been integrated into an application, which practically proved the classifier’s ability to classify timeline tweets of the user.

Keywords: corpus creation, feature extraction, machine learning, short text classification, social media, support vector machine, Twitter

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Authors: Seynabou Toure, Oumar Diop, Kidiyo Kpalma, Amadou S. Maiga

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Color and texture are the two most determinant elements for perception and recognition of the objects in an image. For this reason, color and texture analysis find a large field of application, for example in image classification and segmentation. But, the pioneering work in texture analysis was conducted on grayscale images, thus discarding color information. Many grey-level texture descriptors have been proposed and successfully used in numerous domains for image classification: face recognition, industrial inspections, food science medical imaging among others. Taking into account color in the definition of these descriptors makes it possible to better characterize images. Color texture is thus the subject of recent work, and the analysis of color texture images is increasingly attracting interest in the scientific community. In optical remote sensing systems, sensors measure separately different parts of the electromagnetic spectrum; the visible ones and even those that are invisible to the human eye. The amounts of light reflected by the earth in spectral bands are then transformed into grayscale images. The primary natural colors Red (R) Green (G) and Blue (B) are then used in mixtures of different spectral bands in order to produce RGB images. Thus, good color texture discrimination can be achieved using RGB under controlled illumination conditions. Some previous works investigate the effect of using different color space for color texture classification. However, the selection of the best performing color space in land-sea segmentation is an open question. Its resolution may bring considerable improvements in certain applications like coastline detection, where the detection result is strongly dependent on the performance of the land-sea segmentation. The aim of this paper is to present the results of a study conducted on different color spaces in order to show the best-performing color space for land-sea segmentation. In this sense, an experimental analysis is carried out using five different color spaces (RGB, XYZ, Lab, HSV, YCbCr). For each color space, the Haar wavelet decomposition is used to extract different color texture features. These color texture features are then used for Fusion of Over Segmentation (FOOS) based classification; this allows segmentation of the land part from the sea one. By analyzing the different results of this study, the HSV color space is found as the best classification performance while using color and texture features; which is perfectly coherent with the results presented in the literature.

Keywords: classification, coastline, color, sea-land segmentation

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Authors: Lorena Coelho, David Ramada, Catarina Nobre, Joaquim Gaião, Juliana Duarte

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The development of processes that allows the valorization of waste and by-products generated by industries is crucial to promote symbiotic relationships between different sectors and is mandatory to “close the loop” in the circular economy paradigm. In recent years, by-products and waste from agro-food and forestry sector have attracted attention due to their potential application and technical characteristics. The extraction of bio-based active compounds to be reused is in line with the circular bioeconomy concept trends, combining the use of renewable resources with the process’s circularity, aiming the waste reduction and encouraging reuse and recycling. Among different types of bio-based materials, which are being explored and can be extracted, proteins fractions are becoming an attractive new raw material. Within this context, BioTrace4Leather project, a collaboration between two Technological Centres – CeNTI and CTIC, and a company of Tanning and Finishing of Leather – Curtumes Aveneda, aims to develop innovative and biologically sustainable solutions for leather industry and accomplish the market circularity trends. Specifically, it aims to the valorisation of waste and by-products from the tannery industry through proteins extraction and the development of an innovative and biologically sustainable materials. The achieved results show that keratin, gelatine, and collagen fractions can be successfully extracted from hair and leather bovine waste. These products could be reintegrated into the industrial manufacturing process to attain innovative and functional textile and leather substrates. ACKNOWLEDGEMENT This work has been developed under BioTrace4Leather scope, a project co-funded by Operational Program for Competitiveness and Internationalization (COMPETE) of PORTUGAL2020, through the European Regional Development Fund (ERDF), under grant agreement Nº POCI-01-0247-FEDER-039867.

Keywords: leather by-products, circular economy, sustainability, protein fractions

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Authors: Andi Setiawan, Annisa Ulfah Pristya

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Indonesian rubber plant area reached 2.9 million hectares with productivity reached 1.38 million. High rubber productivity is directly proportional to the amount of waste produced rubber processing industry. Rubber industry would produce a negative impact on the rubber industry in the form of environmental pollution caused by waste that has not been treated optimally. Rubber industrial wastewater containing high-nitrogen compounds (nitrate and ammonia) and phosphate compounds which cause water pollution and odor problems due to the high ammonia content. On the other hand, demand for NPK fertilizers in Indonesia continues to increase from year to year and in need of ammonia and phosphate as raw material. Based on domestic demand, it takes a year to 400,000 tons of ammonia and Indonesia imports 200,000 tons of ammonia per year valued at IDR 4.2 trillion. As well, the lack of phosphoric acid to be imported from Jordan, Morocco, South Africa, the Philippines, and India as many as 225 thousand tons per year. During this time, the process of wastewater treatment is generally done with a rubber on the tank to contain the waste and then precipitated, filtered and the rest released into the environment. However, this method is inefficient and thus require high energy costs because through many stages before producing clean water that can be discharged into the river. On the other hand, Indonesia has the potential of pineapple fruit can be harvested throughout the year in all of Indonesia. In 2010, production reached 1,406,445 tons of pineapple in Indonesia or about 9.36 percent of the total fruit production in Indonesia. Increased productivity is directly proportional to the amount of pineapple waste pineapple leaves are kept continuous and usually just dumped in the ground or disposed of with other waste at the final disposal. Through Eco-Nanofiltration Membrane-Based Fiber Pineapple leaves Waste so that environmental problems can be solved efficiently. Nanofiltration is a process that uses pressure as a driving force that can be either convection or diffusion of each molecule. Nanofiltration membranes that can split water to nano size so as to separate the waste processed residual economic value that N and P were higher as a raw material for the manufacture of NPK fertilizer to overcome the crisis in Indonesia. The raw materials were used to manufacture Eco-Nanofiltration Membrane is cellulose from pineapple fiber which processed into cellulose acetate which is biodegradable and only requires a change of the membrane every 6 months. Expected output target is Green eco-technology so with nanofiltration membranes not only treat waste rubber industry in an effective, efficient and environmentally friendly but also lowers the cost of waste treatment compared to conventional methods.

Keywords: biodegradable, cellulose diacetate, fertilizers, pineapple, rubber

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Authors: Cameron Jones

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Emissions, produced by landfill waste from demolished obsolete buildings, have a damaging effect on both the Earth’s climate and human health. The philosophical theory of material vitalism - the potential for materials to react and emit harmful pollutants - therefore defines this construction and demolition waste (CDW) as having vitality. The European Union’s ‘Circular Economic Action Plan’ (CEAP) aims to mitigate the effects of CDW by prioritising the circularity of building materials. This dissertation examines how the philosophical theory of material vitalism can make an environmentally responsible contribution to CDW policy. The CEAP and Silvertown Quays development are used as case studies for the application of vitalism to policy revision. The study concludes that vitalism has a positive role to play in informing CDW policy, although its contribution is stronger in some areas. This is established by first appraising the aspects that relate to the obsolescence of buildings outlined in the EU’s existing CDW policies. Next, these policy directives are compared with the CE principles employed in the Silvertown Quays development. Subsequently, a keyword analysis model is used to categorise the language used in the CEAP, demonstrating how socio-political approaches to the CE and strategies to address resource scarcity could be strengthened to represent the EU’s policy aspirations more effectively. Recommendations are then made on how material vitalism could be utilised to strengthen legislation, arguing that a notable contribution can be made in most policy areas. Finally, theoretical testing of the impact of these revisions to policy on the case study development identified some practicalities for consideration in improving waste management outcomes.

Keywords: vitalism, construction waste, obsolescence, political ecology, exceptionalism

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Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

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Authors: Vesna Pungercar, Florian Musso

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Identifying materials that can substitute rare or expensive natural resources is one of the key challenges for improving resource efficiency in the building sector. With a growing world population and rising living standards, more and more salt is produced as waste through seawater desalination and potash mining processes. Unfortunately, most of the salt is directly disposed of into nature, where it causes environmental pollution. On the other hand, salt is affordable, is used therapeutically in various respiratory treatments, and can store humidity and heat. It was, therefore, necessary to determine salt materials already in use in building construction and their hygrothermal properties. This research aims to identify salt materials from different scientific branches and historically, to investigate their properties and prioritize the most promising salt materials for indoor applications in a thermal envelope. This was realized through literature review and classification of salt materials into three groups (raw salt materials, composite salt materials, and processed salt materials). The outcome of this research shows that salt has already been used as a building material for centuries and has a potential for future applications due to its hygrothermal properties in a thermal envelope.

Keywords: salt, building material, hygrothermal properties, environment

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Authors: Ann Mariya Jose, Ashfina T.

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Construction and demolition waste is one of the rising concerns globally due to the amount of waste generated annually, the area taken up by landfills, and the adverse environmental impacts that follow. One of the primary causes of the rise in construction and demolition waste is a lack of facilities and knowledge for incorporating recycled materials into new construction. Bricks are a conventional material that has been used for construction for centuries, and Autoclave Aerated Concrete (AAC) blocks are a new emergent material in the market. This study evaluates the impact brick walls, and AAC block walls have on the environment using the tool One Click LCA, considering three End of Life (EoL) scenarios: the materials are landfilled, recycled, and reused in a new building. The final objective of the study is to evaluate the environmental impact caused by these two different walls on the environmental factors such as Global Warming Potential (GWP), Acidification Potential (AP), Eutrophication Potential (EP), Ozone Depletion Potential (ODP), and Photochemical Ozone Creation Potential (POCP). The findings revealed that the GWP caused by landfilling is 16 times higher in bricks and 22 times higher in AAC blocks when compared to the reuse of materials. The study recommends the effective use of AAC blocks in construction and reuse of the same to reduce the overall emissions to the environment.

Keywords: construction and demolition waste, environmental impact, life cycle impact assessment, material recycling

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Authors: Eiman Kattan, Hong Wei

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In using Convolutional Neural Network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to evaluate the impact of a range of parameters in CNN architecture i.e. AlexNet on land cover classification based on four remotely sensed datasets. The evaluation tests the influence of a set of hyperparameters on the classification performance. The parameters concerned are epoch values, batch size, and convolutional filter size against input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters using two implementing approaches, named pertained and fine-tuned. We first explore the number of epochs under several selected batch size values (32, 64, 128 and 200). The impact of kernel size of convolutional filters (1, 3, 5, 7, 10, 15, 20, 25 and 30) was evaluated against the image size under testing (64, 96, 128, 180 and 224), which gave us insight of the relationship between the size of convolutional filters and image size. To generalise the validation, four remote sensing datasets, AID, RSD, UCMerced and RSCCN, which have different land covers and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training and testing. The results have shown that increasing the number of epochs leads to a higher accuracy rate, as expected. However, the convergence state is highly related to datasets. For the batch size evaluation, it has shown that a larger batch size slightly decreases the classification accuracy compared to a small batch size. For example, selecting the value 32 as the batch size on the RSCCN dataset achieves the accuracy rate of 90.34 % at the 11th epoch while decreasing the epoch value to one makes the accuracy rate drop to 74%. On the other extreme, setting an increased value of batch size to 200 decreases the accuracy rate at the 11th epoch is 86.5%, and 63% when using one epoch only. On the other hand, selecting the kernel size is loosely related to data set. From a practical point of view, the filter size 20 produces 70.4286%. The last performed image size experiment shows a dependency in the accuracy improvement. However, an expensive performance gain had been noticed. The represented conclusion opens the opportunities toward a better classification performance in various applications such as planetary remote sensing.

Keywords: CNNs, hyperparamters, remote sensing, land cover, land use

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Authors: Alma Khasenovna Zhakina, Arnt Oxana Vasilievna, Vasilets Evgeny Petrovich

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The aim of this project is to develop methods for obtaining new molecularly imprinted polymers from coal waste to study their structure, structural and morphological features and properties. Recently, the development of molecularly imprinted polymers has become one of the hot topics for researchers. Modern research indicates the broad prospects of rapidly developing molecular imprinting technologies for creating a new generation of sorption materials. The attractiveness of this area of research lies in the fact that the use of imprinted polymers is not limited to scientific research; they are already being introduced in the chemical, pharmaceutical and biotechnological industries, primarily at the stages of purification of the final product. For the use of molecularly imprinted polymers in the development of sorption material, their ability to selectively remove pollutants, including trace concentrations, is of fundamental importance, and the exceptional stability of polymeric materials under harsh conditions makes it possible to simplify the process of water purification as a whole. The scientific and technical effect is associated with the development of technologies for the production of new molecularly imprinted polymers, the establishment of optimal conditions for their production and the creation of effective imprinted sorbents on their basis for wastewater treatment from heavy metals. The social effect is due to the fact that the use of coal waste as a feedstock for the production of imprinted sorbents will make it possible in the future to create new industries with additional jobs and obtain competitive multi-purpose products. The economic and multiplier effect is associated with the low cost of the final product due to the involvement of local coal waste in the production, reduction of transport, customs and other costs.

Keywords: imprinted polymers, coal waste, polymerization, template, customized sorbents

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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Authors: Altilio Rosa, Chirico Francesco, Foglia Goffredo

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In recent years, the number of Unmanned Aerial Vehicles (UAVs) has significantly increased. The rapid development of commercial and recreational drones makes them an important part of our society. Despite the growing list of their applications, these vehicles pose a huge threat to civil and military installations: detection, classification and neutralization of such flying objects become an urgent need. Radar is an effective remote sensing tool for detecting and tracking flying objects, but scenarios characterized by the presence of a high number of tracks related to flying birds make especially challenging the drone detection task: operator PPI is cluttered with a huge number of potential threats and his reaction time can be severely affected. Flying birds compared to UAVs show similar velocity, RADAR cross-section and, in general, similar characteristics. Building from the absence of a single feature that is able to distinguish UAVs and birds, this paper uses a multiple features approach where an original feature selection technique is developed to feed binary classifiers trained to distinguish birds and UAVs. RADAR tracks acquired on the field and related to different UAVs and birds performing various trajectories were used to extract specifically designed target movement-related features based on velocity, trajectory and signal strength. An optimization strategy based on a genetic algorithm is also introduced to select the optimal subset of features and to estimate the performance of several classification algorithms (Neural network, SVM, Logistic regression…) both in terms of the number of selected features and misclassification error. Results show that the proposed methods are able to reduce the dimension of the data space and to remove almost all non-drone false targets with a suitable classification accuracy (higher than 95%).

Keywords: birds, classification, machine learning, UAVs

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Authors: Kemal Comakli, Meryem Terhan

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In this study, the potential of heat recovery from waste flue gas was examined in 60 MW district heating system of a university, and fuel saving was aimed by using the recovered heat in the system as a source again. Various scenarios are intended to make use of waste heat. For this purpose, actual operation data of the system were taken. Besides, the heat recovery units that consist of heat exchangers such as flue gas condensers, economizers or air pre-heaters were designed theoretically for each scenario. Energy analysis of natural gas-fired boiler’s exhaust flue gas in the system, and economic analysis of heat recovery units to predict payback periods were done. According to calculation results, the waste heat loss ratio from boiler flue gas in the system was obtained as average 16%. Thanks to the heat recovery units, thermal efficiency of the system can be increased, and fuel saving can be provided. At the same time, a huge amount of green gas emission can be decreased by installing the heat recovery units.

Keywords: heat recovery from flue gas, energy analysis of flue gas, economical analysis, payback period

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Authors: Preshanthan Moodley, E. B. Gueguim-Kana

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The objective of this study was to develop a method to pretreat sugarcane leaf waste using microwave-assisted (MA) inorganic salt. The effects of process parameters of salt concentration, microwave power intensity and pretreatment time on reducing sugar yield from enzymatically hydrolysed sugarcane leaf waste were investigated. Pretreatment models based on MA-NaCl, MA-ZnCl2 and MA-FeCl3 were developed. Maximum reducing sugar yield of 0.406 g/g was obtained with 2 M FeCl3 at 700W for 3.5 min. Scanning electron microscopy (SEM) and Fourier Transform Infrared analysis (FTIR) showed major changes in lignocellulosic structure after MA-FeCl3 pretreatment with 71.5 % hemicellulose solubilization. This pretreatment was further assessed on sorghum leaves and Napier grass under optimal MA-FeCl3 conditions. A 2 fold and 3.1-fold increase in sugar yield respectively were observed compared to previous reports. This pretreatment was highly effective for enhancing enzymatic saccharification of lignocellulosic biomass.

Keywords: acid, pretreatment, salt, sugarcane leaves

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Authors: Valeria Corinaldesi

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In general, for self-compacting concrete production, a high volume of very fine materials is necessary in order to make the concrete more fluid and cohesive. For this purpose, either rubble powder (which is a powder obtained from suitable treatment of rubble from building demolition) or ash from municipal solid waste incineration was used as mineral addition in order to ensure adequate rheological properties of the self-compacting concrete in the absence of any viscosity modifying admixture. Recycled instead of natural aggregates were used by completely substituting the coarse aggregate fraction. The fresh concrete properties were evaluated through the slump flow, the V-funnel and the L-box test. Compressive strength and segregation resistance were also determined. The results obtained showed that self-compacting concrete could be successfully developed by incorporating both recycled aggregates and waste powders with an improved quality of the concrete surface finishing. This encouraging goal, beyond technical performance, matches with the more and more widely accepted sustainable development issues.

Keywords: sustainable concrete, self compacting concrete, municipal solid waste, recycled aggregate, sustainable building

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Authors: Tamas Madl

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Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

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Authors: Pam Bitrus Marcus

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Compounds in Berom, a Niger-Congo language that is spoken in parts of central Nigeria, have been understudied, and the semantics of N-V nominal compounds have not been sufficiently delineated. This study describes the lexical classification of compounds in Berom and, specifically, examines the semantics of nominal compounds with N-V constituents. The study relied on a data set of 200 compounds that were drawn from Bere Naha (a newsletter publication in Berom). Contrary to the nominalization process in defining the lexical class of compounds in languages, the study revealed that verbal and adjectival classes of compounds are also attested in Berom and N-V nominal compounds have an agentive or locative interpretation that is not solely determined by the meaning of the constituents of the compound but by the context of the usage.

Keywords: berom, berom compounds, nominal compound, N-V compounds

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Authors: Hamidreza Fallah Lajimi, Elham Karami, Alireza Arab, Fatemeh Alinasab

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Being responsive is an increasingly important skill for firms in today’s global economy; thus firms must be agile. Naturally, it follows that an organization’s agility depends on its supply chain being agile. However, achieving supply chain agility is a function of other abilities within the organization. This paper analyses results from a survey of 71 Iran manufacturing companies in order to identify some of the factors for agile organizations in managing their supply chains. Then we classification this company in four cluster with fuzzy c-mean technique and with Four validations functional determine automatically the optimal number of clusters.

Keywords: agile supply chain, clustering, fuzzy clustering, business engineering

Procedia PDF Downloads 697

Authors: Maha Salman, Gada Al-Nuwaibit, Ahmed Al-Haji, Saleh Al-Haddad, Abbas Al-Mesri, Mansour Al-Rugeeb

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The high salinity of oil-produced water and its complicated chemical composition, makes designing a suitable treatment system for oil-produced water is extremely difficult and costly. On the current study, a new innovative method was proposed to treat the complicated oil-produced water through a simple mixing with brine stream produced from waste water treatment plant. The proposal will investigate the scaling potential of oil-produce water, seawater and the selected brine water (BW) produced from Sulaibiya waste water treatment and reclamation plant (SWWTRP) before and after the mixing with oil-produced water, and will calculate the scaling potential of all expected precipitated salts using different conversion and different % of mixing to optimize the % of mixing between the oil-produced water and the selected stream. The result shows a great, feasible and economic solution to treat oil produced with a very low capital cost.

Keywords: brine water, oil-produced water, scaling potential, Sulaibiyah waste water and reclaminatin plant

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Authors: Mourad Morsli, Mohamed Tahiri, Azzedine Samdi

Abstract:

The Cities are the urbanized territories most favorable to the consumption of resources (materials, energy). In Morocco, the economic capital Casablanca is one of them, with its 4M inhabitants and its 60% share in the economic and industrial activity of the kingdom. In the absence of legal status in force, urban development has favored the generation of millions of tons of demolition and construction waste scattered in open spaces causing a significant nuisance to the environment and citizens. Hence the main objective of our work is to valorize concrete waste. The representative wastes are mainly concrete, concrete, and fired clay bricks, ceramic tiles, marble panels, gypsum, and scrap metal. The work carried out includes: geolocation with a combination of artificial intelligence, GIS, and Google Earth, which allowed the estimation of the quantity of these wastes per site; then the sorting, crushing, grinding, and physicochemical characterization of the collected samples allowed the definition of the exploitation ways for each extracted fraction for integrated management of the said wastes. In the present work, we proceeded to the exploitation of the fractions obtained after sieving the representative samples to incorporate them in the manufacture of new ecological materials for construction. These formulations prepared studies have been tested and characterized: physical criteria (specific surface, resistance to flexion and compression) and appearance (cracks, deformation). We will present in detail the main results of our research work and also describe the specific properties of each material developed.

Keywords: demolition and construction waste, GIS combination software, inert waste recovery, ecological materials, Casablanca, Morocco

Procedia PDF Downloads 123

Authors: Chibuzo Okoye Daniels, Gillian Glegg, Lynda Rodwell

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The continuously growing quantity of very slow degrading litter deliberately discarded into the coastal waters around Lagos as marine debris is obvious. What is not known is how to tackle this problem to reduce its prevalence and impact on the environment, economy and community. To identify ways of tackling the marine debris problem two case study areas (Ikoyi and Victoria Islands of Lagos State) were used to assess the root causes, the threat posed by marine debris in the coastal waters around Lagos and the efficacy of current instruments, programmes and initiatives that address marine debris in the study areas. The following methods were used: (1) Self-completed questionnaires for households and businesses within the study areas; (2) Semi-structured interviews with key stakeholders; (3) Observational studies of waste management from collection to disposal and waste management facilities for waste originating from land and maritime sources; (4) Beach surveys and marine debris surveys on shorelines and ports; and (5) Fishing for marine debris. Results of this study identified the following root causes: (1) Indiscriminate human activities and behaviors, and lack of awareness on the part of the main stakeholders and the public of the potential consequences of their actions; (2) Poor solid waste management practices; (3) Lack of strict legal frameworks addressing waste and marine debris problem; and (4) Disposal of non-degradable wastes into domestic sewer system and open streets drains. To effectively tackle marine debris problem in the study areas, adequate, appropriate and cost effective solutions to the above mentioned root causes needs to be identified and effectively transferred for implementation in the study areas.

Keywords: marine debris problem, Lagos state, litter, coastal waters

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Authors: Rajkumar Ghosh, Bhabani Prasad Mukhopadhay, Ananya Mukhopadhyay, Harendra Nath Bhattacharya

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This study investigates the global issue of electronic waste (e-waste), focusing on its prevalence in India and other regions. E-waste has emerged as a significant worldwide problem, with India contributing a substantial share of annual e-waste generation. The primary sources of e-waste in India are computer equipment and mobile phones. Many developed nations utilize India as a dumping ground for their e-waste, with major contributions from the United States, China, Europe, Taiwan, South Korea, and Japan. The study identifies Maharashtra, Tamil Nadu, Mumbai, and Delhi as prominent contributors to India's e-waste crisis. This issue is contextualized within the broader framework of the United Nations' 2030 Agenda for Sustainable Development, which encompasses 17 Sustainable Development Goals (SDGs) and 169 associated targets to address poverty, environmental preservation, and universal prosperity. The study underscores the interconnectedness of e-waste management with several SDGs, including health, clean water, economic growth, sustainable cities, responsible consumption, and ocean conservation. Central Pollution Control Board (CPCB) data reveals that e-waste generation surpasses that of plastic waste, increasing annually at a rate of 31%. However, only 20% of electronic waste is recycled through organized and regulated methods in underdeveloped nations. In Europe, efficient e-waste management stands at just 35%. E-waste pollution poses serious threats to soil, groundwater, and public health due to toxic components such as mercury, lead, bromine, and arsenic. Long-term exposure to these toxins, notably arsenic in microchips, has been linked to severe health issues, including cancer, neurological damage, and skin disorders. Lead exposure, particularly concerning for children, can result in brain damage, kidney problems, and blood disorders. The study highlights the problematic transboundary movement of e-waste, with approximately 352,474 metric tonnes of electronic waste illegally shipped from Europe to developing nations annually, mainly to Africa, including Nigeria, Ghana, and Tanzania. Effective e-waste management, underpinned by appropriate infrastructure, regulations, and policies, offers opportunities for job creation and aligns with the objectives of the 2030 Agenda for SDGs, especially in the realms of decent work, economic growth, and responsible production and consumption. E-waste represents hazardous pollutants and valuable secondary resources, making it a focal point for anthropogenic resource exploitation. The United Nations estimates that e-waste holds potential secondary raw materials worth around 55 billion Euros. The study also identifies numerous challenges in e-waste management, encompassing the sheer volume of e-waste, child labor, inadequate legislation, insufficient infrastructure, health concerns, lack of incentive schemes, limited awareness, e-waste imports, high costs associated with recycling plant establishment, and more. To mitigate these issues, the study offers several solutions, such as providing tax incentives for scrap dealers, implementing reward and reprimand systems for e-waste management compliance, offering training on e-waste handling, promoting responsible e-waste disposal, advancing recycling technologies, regulating e-waste imports, and ensuring the safe disposal of domestic e-waste. A mechanism, Buy-Back programs, will compensate customers in cash when they deposit unwanted digital products. This E-waste could contain any portable electronic device, such as cell phones, computers, tablets, etc. Addressing the e-waste predicament necessitates a multi-faceted approach involving government regulations, industry initiatives, public awareness campaigns, and international cooperation to minimize environmental and health repercussions while harnessing the economic potential of recycling and responsible management.

Keywords: e-waste management, sustainable development goal, e-waste disposal, recycling technology, buy-back policy

Procedia PDF Downloads 71

Authors: Belayneh Matebie, Michael Melese

Abstract:

The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

Procedia PDF Downloads 37