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Search results for: ALTERNATIVE FUELS CO-GASIFICATION DUAL-FUEL ENGINE MACHINE LEARNING RENEWABLE ENERGY OPTIMIZATION
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Experimental tuning of AuAg nanoalloy plasmon resonances assisted by machine learning method
PublicationPlasmonic nanostructures based on AuAg nanoalloys were fabricated by thermal annealing of metallic films in an argon atmosphere. The nanoalloys were chosen because they can extend the wavelength range in which plasmon resonance occurs and thus allow the design of plasmonic platforms with the desired parameters. The influence of initial fabrication parameters and experimental conditions on the formation of nanostructures was investigated....
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Exergetic Analysis of the nCO2PP Cycle with Particular Reference to the Exergy Destruction of Sewage Sludge Due to Gasification
PublicationAn exergy analysis is carried out on the negative CO2 emission gas power plant (nCO2PP), which integrates the process sections of fuel preparation, power generation and carbon capture. Processes of exergy destruction are studied with particular focus on the process in the gasification unit of the fuel preparation section, where a large amount of exergy is destroyed in various chemical reactions from sewage sludge to producer gas...
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Real and Virtual Instruments in Machine Learning – Training and Comparison of Classification Results
PublicationThe continuous growth of the computing power of processors, as well as the fact that computational clusters can be created from combined machines, allows for increasing the complexity of algorithms that can be trained. The process, however, requires expanding the basis of the training sets. One of the main obstacles in music classification is the lack of high-quality, real-life recording database for every instrument with a variety...
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Evaluation of energy efficiency of vehicles powered by different fuels
PublicationW pracy przedstawiono nową metodę oceny efektywności energetycznej pojazdów zasilanych różnymi paliwami. Analizę można wykonać na podstawie jednego przejazdu w regularnym ruchu miejskim z rejestracją podstawowych parametrów silnika i pojazdu. Warunki eksploatacji identyfikowane są przy użyciu energochłonności jednostkowej, która uwzględnia zarówno wpływ warunków zewnętrznych jak również styl jazdy kierowcy.
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Comparison of energy efficiency of vehicles powered by different fuels
PublicationNajpopularniejsza metoda oceny efektywności energetycznej pojazdów samochodowych polega na porównywaniu przebiegowego zużycia paliwa osiągniętego w warunkach wybranego testu homologacyjnego. Warunki eksploatacji, zdefiniowane za pomocą przebiegów prędkości w czasie, dotyczą najczęściej tylko dwóch kategorii: jazdy miejskiej i pozamiejskiej. Problemy wynikające z takiego sposobu postępowania zostały omówione na przykładzie analizy...
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Smart heating system for home extending utilization of renewable energy sources
PublicationIn the paper a modern approach to smart home heating is presented. Proposed solution utilizes at least two low-polluting energy source technologies. The main idea is to connect well known ecological energy sources in a way that they can support each other and minimize risks of failure when using single system or even both of them but managed in separate way. Considered energy technologies, used separatelly, have disadvan-tages,...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublicationPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublicationPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile strengths...
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Platelet RNA Sequencing Data Through the Lens of Machine Learning
PublicationLiquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability...
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Application of road map of operating condition for estimation of fuel and electric energy consumption from city transport
PublicationThe paper presents procedure of data collecting and generation of road map of operating condition in the selected urban area. This map allows forecasting the selected vehicle operating parameters for the assumed road. The main parameters calculated using the road maps of operating conditions are: total energy spent to drive the selected vehicle, consumed fuel, travel time, average speed of travel, CO2 emissions. Presented example...
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Optimization of Train Energy Cooperation Using Scheduled Service Time Reserve
PublicationThe main aim of the paper was to develop an innovative approach to the preliminary estimation possibility of train energy cooperation based on data from timetables, without traction calculations. The article points out the need to strive for sustainable and environmentally friendly transport. It was pointed out that rail transport using electric traction is one of the more ecological branches of transport. It also offers a number...
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DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
PublicationWe present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can...
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Renewable and Sustainable Energy: Current State and Prospects
PublicationThe last two decades of the twentieth century represented a period of above-average, systematic growth of formal and informal interdependencies between economies of different countries and between world markets. The intensity, magnitude, and diversity of these interdependencies have never been recorded before in economic history, and the market transformations taking place have been referred to in the literature as the process...
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Solubility of dapsone in deep eutectic solvents: Experimental analysis, molecular insights and machine learning predictions
PublicationBackground. Dapsone (DAP) is an anti-inflammatory and antimicrobial active pharmaceutical ingredient used to treat, e.g., AIDS-related diseases. However, low solubility is a feature hampering its efficient use. Objectives. First, deep eutectic solvents...
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Alternative Energy: Photovoltaic Modules and Systems
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Alternative Energy: Photovoltaic Solar Cells
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Hybrid Multi-Criteria Method of Analyzing the Location of Distributed Renewable Energy Sources
PublicationThis paper presents the development and the application of a hybrid multi-criteria method, the combination of the Analytic Hierarchy Process (AHP), and numerical taxonomy (NT),to support the decision making on the location of distributed renewable energy sources meetingvarious types of assessment criteria. Finding criteria weights, using the AHP method, eliminates thedisadvantage of NT—which, in current form, is defined by its...
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Dimethyl ether (DME) as potential environmental friendly fuel
PublicationIn recent years, there has been a growing interest in replacing petroleum fuels with so-called second generation environmental friendly fuels. Compared to traditional petroleum fuels dimethyl ether (DME) could be used as a clean high-efficiency compression ignition fuel with reduced particulate matter (PM), sulfur oxides (SOx), hydrocarbons (HC), carbon monoxide (CO) as well as combustion noise. Compared to some of the other leading...
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Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries
PublicationCatheter-induced dissections (CID) of coronary arteries and/or the aorta are among the most dangerous complications of percutaneous coronary procedures, yet the data on their risk factors are anecdotal. Logistic regression and five more advanced machine learning techniques were applied to determine the most significant predictors of dissection. Model performance comparison and feature importance ranking were evaluated. We identified...
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Assessment Of the Relevance of Best Practices in The Development of Medical R&D Projects Based on Machine Learning
PublicationMachine learning has emerged as a fundamental tool for numerous endeavors within health informatics, bioinformatics, and medicine. However, novices among biomedical researchers and IT developers frequently lack the requisite experience to effectively execute a machine learning project, thereby increasing the likelihood of adopting erroneous practices that may result in common pitfalls or overly optimistic predictions. The paper...
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Power and heat from renewable energy sources on energy market in Poland
PublicationPrzedstawiono aktualne uwarunkowania rynków energii w Polsce w odniesieniu do źródeł odnawialnych. Przedstawiono perspektywy rozwojowe. Omówiono wyniki opłacalności budowy małego bloku skojarzonego opalanego słomą.
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Proximal primal–dual best approximation algorithm with memory
PublicationWe propose a new modified primal–dual proximal best approximation method for solving convex not necessarily differentiable optimization problems. The novelty of the method relies on introducing memory by taking into account iterates computed in previous steps in the formulas defining current iterate. To this end we consider projections onto intersections of halfspaces generated on the basis of the current as well as the previous...
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Reinforcement Learning Algorithm and FDTD-based Simulation Applied to Schroeder Diffuser Design Optimization
PublicationThe aim of this paper is to propose a novel approach to the algorithmic design of Schroeder acoustic diffusers employing a deep learning optimization algorithm and a fitness function based on a computer simulation of the propagation of acoustic waves. The deep learning method employed for the research is a deep policy gradient algorithm. It is used as a tool for carrying out a sequential optimization process the goal of which is...
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Asphalt pavement structure optimization with alternative materials
PublicationThe paper briefly describes modern method assessment of the pavement structure based on the simplified viscoelastic continuum damage (S-VECD) model. The method was used to compare two types of pavement structures. There were analysed classical cstructures with asphalt concretes with neat bitumen and innovative one- or two layered structures with SMA 16 with highly polymer modified bitumen (HiMA). Pavement structures using SMA 16...
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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
PublicationMany studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random...
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A machine learning approach to classifying New York Heart Association (NYHA) heart failure
PublicationAccording to the European Society of Cardiology, globally the number of patients with heart failure nearly doubled from 33.5 million in 1990 to 64.3 million in 2017, and is further projected to increase dramatically in this decade, still remaining a leading cause of morbidity and mortality. One of the most frequently applied heart failure classification systems that physicians use is the New York Heart Association (NYHA) Functional...
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COVID-19 severity forecast based on machine learning and complete blood count data
PublicationProper triage of COVID-19 patients is a key factor in eective case management, especially with limited and insucient resources. In this paper, we propose a machine-aided diagnostic system to predict how badly a patient with COVID-19 will develop disease. The prognosis of this type is based on the parameters of commonly used complete blood count tests, which makes it possible to obtain data from a wide range of patients.We chose...
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COVID-19 severity forecast based on machine learning and complete blood count data
PublicationProper triage of COVID-19 patients is a key factor in eective case management, especially with limited and insucient resources. In this paper, we propose a machine-aided diagnostic system to predict how badly a patient with COVID-19 will develop disease. The prognosis of this type is based on the parameters of commonly used complete blood count tests, which makes it possible to obtain data from a wide range of patients.We chose...
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Clean energy in the European Union: Transition or evolution?
PublicationIn this paper, we analyse two phenomena. First, the relationship between greenhouse gases emission and effectiveness of the European Union energy policies and second the transition from the fossil fuels to renewable energy sources. We run two-step data analysis concerning 25 European Union member states in the period from 1990 to 2018. We use information on greenhouse gases emission, introduction of new energy policies, source...
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Modeling and optimization of chemical-treated torrefaction of wheat straw to improve energy density by response surface methodology
PublicationToday, torrefaction is important technique for extending the potential of biomass for improvement of energy density. The independent variables investigated for torrefaction study were temperature, retention time, acid concentration, and particle size. The experiment was designed by central composite design (CCD) method using design expert (version 11). The three dependent variables were higher heating value (HHV), energy enhancement...
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Renewable Resources for Polyurethanes and Polyurethane Composites: A Review
PublicationEach year, more than two million tons of polyurethane is produced in the EU by reacting isocyanates with polyols made from fossil fuel. In addition, there are appreciable quantities of petroleum based functional additives applied in the industry nowadays for both polyols and polyurethane materials. It is therefore of key importance to develop sustainable economically viable polyols with enhanced functionalities, and thereby reducing...
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Optimization of the efficiency of braking energy recovery in rail transport by changing arrival time
PublicationThe article refers to the previous work of the authors, in which the model of traffic organization of cooperating trains including the optimization of the use of energy returned to the catenary was presented. In the presented article, the model was modified by changing the main control variable, which affects the efficient use of energy. Departure time was changed for the arrival time of the train to the stop or station. The optimization...
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Exergy analysis of a negative CO2 emission gas power plant based on water oxy-combustion of syngas from sewage sludge gasification and CCS
PublicationA power cycle with water-injected oxy-combustion (water cycle) is investigated by exergy analysis. It is fueled with syngas (aka. producer gas) from gasification of sewage sludge. The cycle is equipped with a spray-ejector condenser (SEC). CO2 is separated and compressed for transportation and storage. The net delivered electric power is 31% of the fuel exergy. The task efficiency is 39% when the flue gas bleed to gasification...
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Optical method supported by machine learning for dynamics of C‐reactive protein concentrations changes detection in biological matrix samples
PublicationIn this article we present the novel spectroscopy method supported with machine learning for real-time detection of infectious agents in wastewater. In the case of infectious diseases, wastewater monitoring can be used to detect the presence of inflammation biomarkers, such as the proposed C-reactive protein, for monitoring inflammatory conditions and mass screening during epidemics for early detection in communities of concern,...
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Optimization of using recuperative braking energy on a double-track railway line
PublicationIn the introduction, possible ways of reusing energy from recuperation are presented. Next, the paper investigates the possibility of using regenerative braking in the range allowed by the detailed timetable by adopting the method of transferring the recovered electric energy directly to the catenary and immediate use of this energy by another train at the same power section. In the main part of the work, it is shown, that the...
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Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification
PublicationLiquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community...
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Energy Versus Throughput Optimisation for Machine-to-Machine Communication
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Speed estimation of a car at impact with a W-beam guardrail using numerical simulations and machine learning
PublicationThis paper aimed at developing a new method of estimating the impact speed of a passenger car at the moment of a crash into a W-beam road safety barrier. The determination of such a speed based on the accident outcomes is demanding, because often there is no access to full accident data. However, accurate determination of the impact speed is one of the key elements in the reconstruction of road accidents. A machine learning algorithm...
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Modeling of entrained flow steam gasification of sewage sludge
PublicationProper management of sewage sludge becomes increasingly problematic due to legal requirements aiming at diminishing environmental impact, as well as rationalizing the utilization from the point of view of logistics. Steam gasification of sewage sludge can result in very good quality of the producer gas. So far, the works have been focused on the gasification in fixed bed gasifiers. However, this does not allow to take full advantage...
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Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
PublicationSince high-quality real data acquired from selected road sections are not always available, a traffic control solution can use data from software traffic simulators working offline. The results show that in contrast to microscopic traffic simulation, the algorithms employing neural networks can work in real-time, so they can be used, among others, to determine the speed displayed on variable message road signs. This paper describes...
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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublicationDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
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Machine-Learning-Powered EM-Based Framework for Efficient and Reliable Design of Low Scattering Metasurfaces
PublicationPopularity of metasurfaces has been continuously growing due to their attractive properties including the ability to effectively manipulate electromagnetic (EM) waves. Metasurfaces comprise optimized geometries of unit cells arranged as a periodic lattice to obtain a desired EM response. One of their emerging application areas is the stealth technology, in particular, realization of radar cross section (RCS) reduction. Despite...
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Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
PublicationHigh-performance alkali-activated concrete (HP-AAC) is acknowledged as a cementless and environmentally friendly material. It has recently received a substantial amount of interest not only due to the potential it has for being used instead of ordinary concrete but also owing to the concerns associated with climate change, sustainability, reduction of CO2 emissions, and energy consumption. The characteristics and amounts of the...
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Microprocessor system for controlling the operation of renewable energy resources
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Machine learning approach to packaging compatibility testing in the new product development process
PublicationThe paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in compatibility tests which are preformed to ensure safety, durability, and efficacy of the finished product for the entire period of its shelf life and consumer use. This kind of testing...
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Thermodynamic Cycles of Air Microturbine Power Plants Working on Biomass Fuels
PublicationThe gas turbine engine is modified to work as an air turbine set which consists, in the simplest arrangement, of a compressor, a heat exchanger and a turbine. Air is a working medium for both: the compressor and the turbine. This kind of air turbine set can be applied in power plants working on biomass fuels. In this solution we can burn fuels of varying parameters in the external combustion chamber without any harmful effects...
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Generalized regression neural network and fitness dependent optimization: Application to energy harvesting of centralized TEG systems
PublicationThe thermoelectric generator (TEG) system has attracted extensive attention because of its applications in centralized solar heat utilization and recoverable heat energy. The operating efficiency of the TEG system is highly affected by operating conditions. In a series-parallel structure, due to diverse temperature differences, the TEG modules show non-linear performance. Due to the non-uniform temperature distribution (NUTD) condition,...
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Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers
PublicationThis chapter presents the application of new information technology in education for the training of air traffic controllers (ATCs). Machine learning, multi-criteria decision analysis, and text analysis as the methods of artificial intelligence for ATCs training have been described. The authors have made an analysis of the International Civil Aviation Organization documents for modern principles of ATCs education. The prototype...
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Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design
PublicationThe design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering...
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Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning
PublicationEvery year in many countries, there are fatal unintentional drownings in different water reservoirs like swimming pools, lakes, seas, or oceans. The existing threats of this type require creating a method that could automatically supervise such places to increase the safety of bathers. This work aimed to create methods and prototype solutions for detecting people bathing in water reservoirs using a multimodal imaging system and...