The production of cement accounts for 5 to 7% of carbon dioxide emissions in the world, and its broad-scale use contributes to climate imbalance. As a solution, biotechnology enables the cultivation of bacteria and fungi for the synthesis of calcium carbonate as one of the main constituents of cement. Through biomineralization, which is the initial driving force for the synthesis of compounds compatible with concrete, and crystallization, these compounds can be delivered to cracks in concrete. Microencapsulation is a method that serves as a clock to determine when crystallization is needed, which is assisted by control factors such as pH and aeration. The present review addresses possibilities of working with bioconcrete, describing the composition of Portland cement, analysis methods, deterioration, as well as environmental and energetic benefits of using such an alternative material. A discussion on carbon credits is also offered. The contents of this paper could strengthen the prosp... [more]

Analyzing energy consumption is currently of great interest to define efficient energy management strategies. In particular, studying the evolution of the behavior of the consumption pattern can allow energy policies to be defined according to the time of the year. In this sense, this work proposes to study the evolution of energy behavior patterns using online clustering techniques. In particular, the centroids of the groups constructed by the techniques will represent their consumption patterns. Specifically, two unsupervised online machine learning techniques ideal for the stated objective will be analyzed, X-Means and LAMDA, since they are capable of varying and adapting the number of clusters at runtime. These techniques are applied to energy consumption data in commercial buildings, making groupings on previous groups, in our case, monthly and quarterly. We compared their performance by analyzing the evolution of the patterns over time. The results are very promising since the qu... [more]

When burning fuel in grate furnaces, supplying the right amount of air to them is as important as the method of air supply. In a furnace with a fixed grate, the supply method of primary air is determined by the distribution of the supplied air stream over time, and in a furnace with a movable grate, the said method involves the distribution of the stream along the active length of the grate. The need to account for air distribution is attributable to complex processes that occur during the combustion process. The paper describes experimental studies aimed at determining the influence of the distribution of the supplied primary air on the emission of CO2, CO, SO2, NOx, and on the content of combustible parts in the slag. In all cases, the total amount of primary air supplied to the process as well as other process control parameters was identical, and only the distribution of primary air was different. The paper proposes the use of a generalized function to describe the distribution of... [more]

Reducing CO2 emissions from coal-fired power plants is an urgent global issue. Effective and precise monitoring of CO2 emissions is a prerequisite for optimizing electricity production processes and achieving such reductions. To obtain the high temporal resolution emissions status of power plants, a lot of research has been done. Currently, typical solutions are utilizing Continuous Emission Monitoring System (CEMS) to measure CO2 emissions. However, these methods are too expensive and complicated because they require the installation of a large number of devices and require periodic maintenance to obtain accurate measurements. According to this limitation, this paper attempts to provide a novel data-driven method using net power generation to achieve near-real-time monitoring. First, we study the key elements of CO2 emissions from coal-fired power plants (CFPPs) in depth and design a regression and physical variable model-based emission simulator. We then present Emission Estimation N... [more]

Because of electricity markets, environmental concerns, transmission constraints, and variable renewable energy sources (VRES), coordinated operation of demand response (DR) and battery energy storage systems (BESS) has become critical. In turn, the optimal coordinated operation of DR and BESS by an entity can affect overall electricity market outcomes and transmission network conditions. The coordinated operation is desirable for the profit-seeking entity, but it may adversely affect the cost and revenues of other market participants or cause system congestion. Though few coordinated operation models already exist, our aim in this research is to provide a novel multi-objective optimization-based methodology for the coordinated operation of DR and BESS to boost market profit. Moreover, another goal is to simultaneously study the combined effects of such coordinated models on transmission networks and electricity markets for the first time. This paper has proposed a new method for coord... [more]

For Brush-less DC motors to function better under various operating settings, such as constant load situations, variable loading situations, and variable set speed situations, speed controller design is essential. Conventional controllers including proportional integral controllers, frequently fall short of efficiency expectations and this is mostly because the characteristics of a Brush-less DC motor drive exhibit non linearity. This work proposes a hybrid gray wolf optimization and proportional integral controller for management of the speed in Brush-less DC motors to address this issue. For constant load conditions, varying load situations and varying set speed situations, the proposed controller’s efficiency is evaluated and contrasted with that of PID controller, PSO-PI controller, and ANFIS. In this study, two PI controller are used to get the more stability of the system based on tuning of their coefficients with meta heuristic method. The simulation findings show that Hybrid GW... [more]

Waste-to-energy processes remain essential to ensure the safe and irreversible removal of materials and substances that are (or have become) unsuitable for reuse or recycling, and hence, to keep intended cycles of materials in the circular economy clean. In this paper, the behavior of inorganic compounds in waste-to-energy combustion processes are discussed from a multi-disciplinary perspective, against a background of ever tightening emission limits and targets of increasing energy efficiency and materials recovery. This leads to the observation that, due to the typical complexity of thermally treated waste, the intelligence of combustion control systems used in state-of-the-art waste-to-energy plants needs to be expanded to better control the behavior of inorganic compounds that typically end up in waste furnaces. This paper further explains how this goal can be achieved by developing (experimentally validated) predictive numerical models that are engineering-based and/or data-driven... [more]

Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation.

In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO2, noise, and relative temperature via our machine learning method and forecasting strategy. The proposed algorithms enhance the energy management system through the optimal use of the electric heating system. The Long Short-Term Memory (LSTM) neural network is a special deep learning strategy for processing time series prediction that has shown promising prediction results in recent years. To improve the performance of the LSTM algorithm, particularly for autocorrelation prediction, we will focus on optimizing weight updates using various approaches such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performances of the proposed methods are evaluated using real available datasets. Test results reveal that the GA and the PSO can forecast the parameters with higher prediction fidelity compared to the LSTM networks. Indeed, all experimental predictions rea... [more]

The functioning of Ukrainian national gas sector is directly dependent on the processes of fuel and energy resources consumption and trends in domestic and foreign markets. Nowadays, the majority of approaches and methods are formed with the obligatory use of expert assessment methods, which, in its turn, predetermines relatively subjective judgments and results. In the process of conducting a comprehensive analysis of financial and economic indicators and those reflecting the results of economic activity of gas distribution network operators functioning in the western region of Ukraine, the following approaches have been used in our study with the involvement of: Altman’s two-factor model; Altman’s five-factor model; Lis’s bankruptcy prediction model; Richard Taffler’s model; Beaver’s coefficient; Tereshchenko’s model and Matviychuk’s model; however, the existing models for diagnosing bankruptcy of enterprises are characterized by ambiguity; as for example, if Lis’s model indicates a... [more]

The recovery of livestock manure, rich in nutrients, as fertilizer in agriculture, could pose the risk of an excessive load of nitrogen on the soil. Ammonia stripping is one of the available technologies for reducing the amount of nitrogen in the digestate obtained by the anaerobic digestion of manure. The study investigated the performance and energy consumption of a full-scale ammonia-stripping plant, equipped with a bubble reactor and working without the use of any alkaline reagent under semi-batch conditions. Stripping tests were conducted on the liquid fraction of the digestate, studying the current and optimized operative conditions of the plant. The main variables influencing the process were pH, temperature, airflow, and feed characteristics. In the experimental tests, the pH spontaneously increased to 10, without dosing basifying agents. Higher temperatures favoured the stripping process, the higher tested value being 68 °C. The airflow was kept equal to 15 Nm3 h−1 m−3digestat... [more]

A reservoir with a thickness less than 0.5 m is generally considered to be a thin reservoir, in which it is difficult to directly identify oil-water layers with conventional logging data, and the identify result coincidence rate is low. Therefore, a support vector machine method (SVM) is introduced in the field of oil-water-dry layer identification. The basic approach is to map the nonlinear problem (input space) to a new high-dimensional feature space through the introduction of a kernel function, and then construct the optimal decision surface in the high-dimensional feature space and conduct sample classification. There are plenty of thin reservoirs in Wangguantun oilfield. Therefore, 63 samples are established by integrating general logging data and oil testing data from the study area, including 42 learning samples and 21 prediction samples, which are normalized. Then, the kernel function is selected, based on previous experience, and the fluid identification model of the thin res... [more]

In the development of unconventional shale resources, production forecasts are fraught with uncertainty, especially in the absence of a full, multi-data study of reservoir characterization. To forecast Duvernay shale gas production in the vicinity of Fox Creek, Alberta, the multi-scale experimental findings are thoroughly evaluated. The relationship between shale gas production and reservoir parameters is assessed using multiple linear regression (MLR). Three hundred and five core samples from fifteen wells were later examined using the MLR technique to discover the fundamental controlling characteristics of shale potential. Quartz, clay, and calcite were found to comprise the bulk of the Duvernay shale. The average values for the effective porosity and permeability were 3.96% and 137.2 nD, respectively, whereas the average amount of total organic carbon (TOC) was 3.86%. The examined Duvernay shale was predominantly deposited in a gas-generating timeframe. As input parameters, the MLR... [more]

Automated fault detection and diagnostics in building systems using machine learning (ML) can be applied to commercial buildings and can result in increased efficiency and savings. Using ML for FDD brings the benefit of advancing the analytics of a building. An automated process was developed to provide ML-based building analytics to building engineers and operators with minimal training. The process can be applied to buildings with a variety of configurations, which saves time and manual effort in a fault analysis. Classification analysis is used for fault detection and diagnostics. An ML analysis is defined which introduces advanced diagnostics with metrics to quantify a fault’s impact in the system and rank detected faults in order of impact severity. Explanations of the methodology used for the ML analysis include a description of the algorithms used. The analysis was applied to a building on the Texas A&M University campus where the results are shown to illustrate the performance... [more]

Because of the social, economic, and environmental issues linked with fossil resources, there is a global interest in finding alternative renewable and sustainable resources for energy and materials production. Biomass could be one such renewable material that is available in large quantities. However, biomass physicochemical properties are a challenge for its industrial application. Recently, the torrefaction process was developed to improve the fuel characteristics of biomass. However, in recent days, energy production has slowly been shifting towards solar and wind, and restrictions on thermal power plants are increasing. Thus, there will be a need to find alternative market opportunities for the torrefaction industry. In that regard, there is a quest to find alternative applications of torrefaction products other than energy production. This paper presents a couple of alternative applications of torrefied biomass. Torrefaction process can be used as a biomass pretreatment option fo... [more]

The goal of this work is to give a full review of how machine learning (ML) is used in thermal comfort studies, highlight the most recent techniques and findings, and lay out a plan for future research. Most of the researchers focus on developing models related to thermal comfort prediction. However, only a few works look at the current state of adaptive thermal comfort studies and the ways in which it could save energy. This study showed that using ML control schemas to make buildings more comfortable in terms of temperature could cut energy by more than 27%. Finally, this paper identifies the remaining difficulties in using ML in thermal comfort investigations, including data collection, thermal comfort indices, sample size, feature selection, model selection, and real-world application.

Bladeless fans are more energy efficient, safer due to the hidden blades, easier to clean, and more adjustable than conventional fans. This paper investigates the influence of the airfoil’s outlet slit thickness on the discharge ratio by varying the outlet slit thickness of an Eppler 473 airfoil from 1.2 mm to 2 mm in intervals of 0.2 mm by using a k-omega SST turbulence model with an all y+ wall treatment used to numerically simulate in CFD. The computational results indicated that smaller slits showed higher discharge ratios. The airfoil with a 1.2 mm slit thickness showed a discharge ratio of 18.78, a 24% increase from the discharge ratio of the 2 mm slit. The effect of outlet angle on the pressure drop across the airfoil was also studied. Outlet angles were varied from 16° to 26° by an interval of 2°. The airfoil profile with a 24° outlet angle showed a maximum pressure difference of 965 Pa between the slit and leading edge. In contrast, the 16° outlet angle showed the least pressu... [more]

The global energy system is undergoing an undeniable change [...]

This paper describes the detailed modelling of a vibration-based miniature piezoelectric device (PD) and the analysis modes of operation and control of a voltage doubler boost converter (VDBC) circuit to find the PD’s optimal operating conditions. The proposed VDBC circuit integrates a conventional voltage doubler (VD) circuit with a step-up DC-DC converter circuit in modes 1−4, while a non-linear synchronisation procedure of a conventional boost converter circuit is employed in modes 5−6. This integration acted as the voltage boost circuit without utilising duty cycles and complex auxiliary switching components. In addition, the circuit does not require external trigger signals to turn on the bidirectional switches. This facilitates the operation of VDBC circuit at very low AC voltage (Vac ≥ 0.5 V). Besides this, the electrical characteristics of VDBC circuit’s input (i.e., PD) perfectly concurs with the studied testing scenarios using impedance power sources (mechanical shaker). Firs... [more]

When a permanent magnet synchronous motor (PMSM) is designed according to the traditional motor design theory, the performance of the motor is often challenging to achieve the desired goal, and further optimization of the motor design parameters is usually required. However, the motor is a strongly coupled, non-linear, multivariate complex system, and it is a challenge to optimize the motor by traditional optimization methods. It needs to rely on reliable surrogate models and optimization algorithms to improve the performance of the PMSM, which is one of the problematic aspects of motor optimization. Therefore, this paper proposes a strategy based on a combination of a high-precision combined surrogate model and the optimization method to optimize the stator and rotor structures of interior PMSM (IPMSM). First, the variables were classified into two layers with high and low sensitivity based on the comprehensive parameter sensitivity analysis. Then, Latin hypercube sampling (LHS) is us... [more]

Distributed energy resources aggregators (DERAs) are permitted to participate in regional wholesale markets in many counties. At present, new market players such as aggregators participate in China’s power market transactions. However, studies related to market trading strategy have mostly focused on centralized wind power and PV generation units. Few studies have been conducted on the decision-making strategies for DERAs in China’s power market. This paper proposes an auxiliary decision-making model for distributed energy systems to participate in the day-ahead market with more reasonable trading strategies. Firstly, the Gaussian mixture model (GMM) is used to deal with the uncertainties of wind power and photovoltaic (PV) output in the distributed energy system. Secondly, the information gap decision theory (IGDT) is used to deal with the uncertainty of price fluctuations in the spot electricity market. Thirdly, according to the different risk preferences of the DERAs facing market p... [more]

This paper presents a practical usability investigation of recurrent neural networks (RNNs) to determine the best-suited machine learning method for estimating electric vehicle (EV) batteries’ state of charge. Using models from multiple published sources and cross-validation testing with several driving scenarios to determine the state of charge of lithium-ion batteries, we assessed their accuracy and drawbacks. Five models were selected from various published state-of-charge estimation models, based on cell types with GRU or LSTM, and optimisers such as stochastic gradient descent, Adam, Nadam, AdaMax, and Robust Adam, with extensions via momentum calculus or an attention layer. Each method was examined by applying training techniques such as a learning rate scheduler or rollback recovery to speed up the fitting, highlighting the implementation specifics. All this was carried out using the TensorFlow framework, and the implementation was performed as closely to the published sources a... [more]

Efficient simulation of the helical coiled once-through steam generator (H-OTSG) is crucial in the design and safety analysis of the high-temperature gas-cooled reactor (HTGR). The physical property and phase transformation of water in the steam generator brings great challenges during simulation. The water properties calculation routine occupies a large part of the computational time in the steam generator solution process. Thus, a thermohydraulic property library is developed based on the IAPWS-IF97 formulation in this work to reduce the computational cost. Here the formulation adopts the backward equation method to avoid iterations in thermodynamic property calculation. Moreover, two Newton-method-based simultaneous solutions are implemented as implicitly nonlinear solvers, including Jacobian-Free Newton−Krylov (JFNK) and Newton−Krylov (NK) methods, due to its excellent computational performance. These simultaneous solution algorithms are combined with the developed water property l... [more]

Building a new power system with renewable energy as its main component is a key measure proposed by China to address the climate change problem. Strengthening demand-side management (DSM) is an important way to promote the development of a new power system. As an important economic incentive measure in DSM, the current TOU tariff is faced with the problem of a weak incentive effect due to the small tariff difference between the peak and valley periods. Against this background, a novel hybrid three-stage seasonal TOU tariff optimization model is proposed in this paper. First, the K-means++ algorithm is adopted to select the typical days of the four seasons through load curve clustering. Then, the price elasticity of the electricity demand model is constructed to calculate the self-elasticity and cross-elasticity in four seasons. Finally, the seasonal TOU tariff optimization model is constructed to determine the optimal TOU tariff. Through the proposed model, the tariff in the peak peri... [more]

Energy is one of the most important aspects of urban development and technological advancements. As its production and consumption are connected to several environmental, social, and economic issues covering all three sustainability pillars, strategic and targeted energy planning is vital to the smooth transition towards a more efficient and greener society. In accordance with the specific priorities of every state, sustainable energy planning should also satisfy the international trends, requirements, and targets, including the global commitments for sustainable development. As of this time, energy transition with further deployment of renewable energy and energy efficiency improvement are the priorities for a sustainable future. However, due to recent global events, a new situation has been established. The COVID-19 pandemic and the ongoing war in Ukraine have caused new circumstances challenging the recognized approaches for an effective sustainable energy strategy. While the global... [more]