Building models and their connected subsystems are often simulated as standalone entities. However, in order to monitor a system′s reactions to changing parameters and to assess its energy efficiency, it must be exposed to the actual dynamic context of the building under study. Hence, frameworks assessing co-operative simulation of buildings and their subsystems should be used. In this study, the Building Control Virtual Test Bed (BCVTB) framework was used for co-simulation of a small-scale building (EEBLab) connected to an Earth-to-air heat exchanger (EAHE). The EnergyPlus tool was used to simulate the indoor air temperature variations within the EEBLab, and MATLAB was used to model the EAHE system and to calculate its performance based on various parameters. The HOLSYS internet of things platform was deployed to monitor and collect the experimental data from the sensors to validate the simulations. A favorable agreement between the experimental and simulation results was obtained, sh... [more]

The biofuel management of a biofuel-penetrated district heating system is complicated due to its association with multiple and polymorphic uncertainties. To handle uncertainties and system dynamic complexities, an inexact two-stage compound-stochastic mixed-integer programming technique is proposed, innovatively based on the integration of different uncertain optimization approaches. The proposed technique can not only address the inexact recourse problems sourced from multiple and compound uncertainties existing in the pre-regulated biofuel supply−demand match mode, but can also quantitatively analyze the conflicts between the economic target that minimizes the system cost and the risk preference that maximizes the heating service satisfaction. The developed model is applied to a real-world biofuel management case study of a district heating system to obtain the optimal biofuel management schemes subject to supply−demand, policy requirement constraints, and the financial minimization... [more]

The harvesting and hauling operations of bioenergy feedstock is an important area in biofuel production. Production costs can be minimized by maintaining optimal machinery units for these operations. The objective of this study is to design an optimal harvesting unit for bioenergy refinery and estimate harvesting and hauling costs of energy cane. A biorefinery with the annual capacity of processing twenty-five million imp. gallons of ethanol were considered. Given the efficiency of harvesting, a two-row soldier system was considered. Considering the year-round supply of energy cane to the refinery, the optimal machinery unit was designed, and the combined operation costs were derived. The average estimated ownership, repair, labor and fuel and lubricant costs of biomass harvest unit were calculated to be $0.50, $0.54, $1.78 and $1.51/mt, respectively. The costs distribution generated showed harvesting and hauling costs could range between $5.47−$9.23/mt of energy cane. The methodology... [more]

Despite the rapid development of electric vehicles, the shrinking number of fossil fuels that are the source of electricity remains conventional. The availability of energy sources and technology is sometimes naturally limited, high-priced, and might be politically circumscribed. This leads to an increased desirability of biodiesel due to its modest and economically higher energy density in comparison to batteries. The palm oil industry accounts for 23% of total deforestation in Indonesia. Contrary to palm oil, pangium edule (PE) is considered more sustainable and it intercrops with most of the forest’s vegetation while supplying biodiesel feedstock. A relatively higher pangium edule methyl ester (PEME) was delivered through PE feedstock, provided that it was processed with a heterogeneous catalyst, K2O/PKS-AC. This feedstock consumed a lower alcohol ratio and had a reasonably swift production process without sacrificing biodiesel quality. Therefore, this study aims to assess the perfo... [more]

The deposition of the Lower Cambrian argillaceous rocks on the Kongquehe slope provides a good opportunity to reconstruct the paleoenvironmental conditions of the Tarim Basin. To explore the paleoredox conditions, paleoclimate, and provenance of this deposit, 21 samples were collected from Well A, and the concentrations of major and trace elements were analyzed. The V/(V + Ni), V/Cr, Ni/Co, U/Th, Uau, and V/Sc ratios indicated that the sediments in the water body from the Lower Cambrian were in a reducing environment, and the degree of reduction weakened from the bottom to the top. The Sr/Cu, Mg/Ca, and Fe/Mn ratios in the sediments revealed that the Lower Cambrian was mainly characterized by a warm and humid paleoclimate, and there may be a paleoclimatic transition toward drought conditions as recorded by the sediments of Xidashan Formation. The chemical alteration index, compositional variation index, plagioclase alteration index values, and Th/U and K/Rb ratios of the Lower Cambrian... [more]

Aiming at the typical faults in the coal mills operation process, the kernel extreme learning machine diagnosis model based on variational model feature extraction and kernel principal component analysis is offered. Firstly, the collected signals of vibration and loading force, corresponding to typical faults of coal mill, are decomposed by variational model decomposition, and the intrinsic model functions at different scales are obtained. Then, the eigenvectors consisting of feature energy and sample entropy in these functions are respectively calculated, and the kernel principal component analysis is used for noise removal and dimensionality reduction. Finally, the kernel extreme learning machine model is trained and tested with the dimension reduced feature vector as input and the corresponding coal mill state as output. The results show that the variational model decomposition extraction can improve the input features of the model compared with the single eigenvector model, and the... [more]

Based on China’s goal of achieving carbon neutrality by 2060, this study focused on its coal gasification in 2010−2019. Carbon emissions were calculated from industrial data, and an LMDt model was established to analyze the influencing factors of carbon emissions. Through scenario analysis, the paths of carbon emission reductions in the chemical industry were analyzed, and their emission reduction potential was estimated. The results showed that the carbon emissions in the chemical industry increased rapidly in 2010−2019, reaching 196 million tons in 2019. The emission structure was the most important factor in mitigating carbon emissions, and the emission intensity, industrial structure, economic development level, and labor force scale had different degrees of promotion effects, of which emission intensity was the strongest. The chemical industry can reach a carbon peak before 2030 under the three analyzed scenarios, and the emission reduction potential is the largest under the landi... [more]

Natural gas, biogas, and refinery gas all include H2S, which has adverse effects not only on the environment and human health but also on the equipment and catalysts that are employed in the relevant processes. H2S is removed from the aforementioned gases using a variety of techniques in order to fulfill the necessary sales criteria and for reasons of safety. The adsorption method stands out among various other approaches due to its straightforward operation, high level of efficiency, and low overall cost. This technique makes use of a variety of adsorbents, such as metal-organic frameworks (MOFs), activated carbon, and zeolites. The use of zeolite-based adsorbents is by far the most common of these various types. This is due to the specific properties of zeolite-based adsorbents, which include a high adsorption capacity, the ability to be regenerated, a high temperature stability, a diversity of types, the possibility of modification, high efficiency, and low cost. In addition, resear... [more]

Renewable energy-based power generation technologies are becoming more and more popular since they represent alternative solutions to the recent economic and environmental problems that modern society is facing. In this sense, the most widely spread applications for renewable energy generation are the solar photovoltaic and wind generation. Once installed, typically outside, the wind generators and photovoltaic panels suffer the environmental effects due to the weather conditions in the geographical location where they are placed. This situation, along with the normal operation of the systems, cause failures in their components, and on some occasions such problems could be difficult to identify and hence to fix. Thus, there are generated energy production stops bringing as consequence economical losses for investors. Therefore, it is important to develop strategies, schemes, and techniques that allow to perform a proper identification of faults in systems that introduce renewable gener... [more]

The future of opencast mining and energy production based on conventional resources is one of the most important issues being discussed in international forums. The whole discussion is becoming increasingly heated and takes on a special significance with the drastic increase in energy commodity prices that has occurred with the outbreak of war in Ukraine. Especially in a country like Poland, these issues are accompanied by heated discussions between miners, the government and citizens. It should be emphasised that Polish lignite mining currently produces about 35% of the cheapest electricity in Poland and also creates many jobs. The aim of this study is to assess the possibility of continuing opencast mining and its impact on rural development—both from an environmental and socio-economic point of view. The study was conducted for two municipalities in Poland where opencast lignite mining plays an important role, namely Kleszczów and Kleczew. As a result, it was found that in the case... [more]

In the present study, energy and exergy analyses of a simple supercritical, a split supercritical and a cascade supercritical CO2 cycle are conducted. The bottoming cycles are coupled with the main two-stroke diesel engine of a 6800 TEU container ship. An economic analysis is carried out to calculate the total capital cost of these installations. The functional parameters of these cycles are optimized to minimize the electricity production cost (EPC) using a genetic algorithm. Exergo-economic and exergo-environmental analyses are conducted to calculate the cost of the exergetic streams and various exergo-environmental parameters. A parametric analysis is performed for the optimum bottoming cycle to investigate the impact of ambient conditions on the energetic, exergetic, exergo-economic and exergo-environmental key performance indicators. The theoretical results of the integrated analysis showed that the installation and operation of a waste heat recovery optimized split supercritical... [more]

In recent years, there has been a dynamic development of photovoltaic materials based on perovskite structures. Solar cells based on perovskite materials are characterised by a relatively high price/performance ratio. Achieving stability at elevated temperatures has remained one of the greatest challenges in the perovskite solar cell research community. However, significant progress in this field has been made by utilising different compositional engineering routes for the fabrication of perovskite semiconductors such as triple cation-based perovskite structures. In this work, a new approach for the rapid analysis of the changes occurring in time in perovskite structures was developed. We implemented a quick and inexpensive method of estimating the ageing of perovskite structures based on an express diagnosis of light reflection in the near-infrared region. The possibility of using optical reflectance in the NIR range (900−1700 nm) to observe the ageing of perovskite structures over ti... [more]

This paper proposes a learning-based finite control set model predictive control (FCS-MPC) to improve the performance of DC-DC buck converters interfaced with constant power loads in a DC microgrid (DC-MG). An approach based on deep reinforcement learning (DRL) is presented to address one of the ongoing challenges in FCS-MPC of the converters, i.e., optimal design of the weighting coefficients appearing in the FCS-MPC objective function for each converter. A deep deterministic policy gradient method is employed to learn the optimal weighting coefficient design policy. A Markov decision method formulates the DRL problem. The DRL agent is trained for each converter in the MG, and the weighting coefficients are obtained based on reward computation with the interactions between the MG and agent. The proposed strategy is wholly distributed, wherein agents exchange data with other agents, implying a multi-agent DRL problem. The proposed control scheme offers several advantages, including pre... [more]

In the publication, the authors put forward a thesis about the low social understanding of the essence of the idea of sustainable development (SD), its assumptions, and difficulties with its implementation. This thesis became the starting point for research on contemporary problems related to the understanding and implementation of the idea of SD in Poland and an inspiration to look at Education for Sustainable Development (ESD). The aim of the study was to get to know the opinions of experts on the statutory definition of SD and social awareness of this idea, and then to relate these opinions to the state of knowledge of students, as well as their opinions and attitudes towards SD. Primary data obtained in the course of surveys among 105 Polish experts (employees of universities, research institutes, government and local government units) and 844 students from several Polish university centres were used. The obtained results indicated gaps in Education for Sustainable Development (ESD... [more]

Eco-driving algorithms optimize the speed profile to reduce the energy consumption of a vehicle. This paper presents an eco-driving algorithm for battery electric powertrains that applies a split loss integration approach to incorporate the component losses. The algorithm consistently uses loss models to overcome the drawbacks of efficiency maps, which cannot represent no-load losses at zero torque. The use of loss models is crucial since the optimal solution includes gliding, during which there are no-load losses. An analysis shows, that state-of-the-art nonlinear programming algorithms cannot represent these no-load losses at zero torque with a small modeling error. To effectively compute the powertrain losses with only a small error in comparison to the measurement data, we introduce a tailored combination of nonlinear inequality constraints that interleave two polynomial fits. This approach can properly represent reality. We parameterize the algorithm and validate the vehicle model... [more]

In the present study, the co-digestion effectiveness of the selected beverage wastes and municipal sewage sludge in two- and three-component mixtures was evaluated. Orange peels and orange pulp, as well as brewery spent grain were applied as co-substrates to sewage sludge at the following doses: 1.5 and 3.0 g of orange peels, 2.5 and 5 g of orange pulp, and 1.5 g brewery spent grain. Mono-digestion of sewage sludge was used as a control. The experiments were performed under mesophilic conditions in batch reactors. As compared to the control, only in the presence of the highest dose of pulp, brewery spent grain and sewage sludge was the increased methane production of 395 mL CH4 g−1 VS accompanying an additional energy profit of 82% observed. Moreover, in this case, the enhanced volatile solids removal and lower accumulation of p-cymene were found. These results were despite the increased limonene and phenol content in the feedstock, confirming a synergistic effect at the highest dose o... [more]

In the present work, to investigate the hydraulic losses and safe operation of nozzle check valves in industrial piping systems, the static characteristics of the valve and its dynamic behavior in the pipeline system were studied using an experimental bench with a visual DN300 nozzle check valve. Besides, basing on the PIV (Particle Image Velocimetry) technique measures the valve steady-state flow field under the different flow rates. The study has shown that as the flow rate rises, the valve disc displacement slowly increases to 44 mm, then rapidly increases to a maximum displacement of 72 mm. When the Reynolds number exceeds 5 × 105, the relationship between pressure drop and flow obeys a quadratic function. The local vortex area formed by the flow passage near the downstream deflector expands with the flow improvement. As the increase of flowrate, at low flow operating conditions, the downstream flow velocity in the local high-speed area near the valve body increases; at medium oper... [more]

High temperature superconductivity is emerging as a solution for lightweight, cost-effective and high-power wind generators. Current injection and maintainment/sustainment in the field winding are obtained by metal current leads which, due to persistent heat conduction and joule loss, are responsible for a large part of the total cryogenic heat load. Slip rings, which further reduce the overall performance and reliability of the system, are also required. In this paper we assess the viability of the HTS dynamo and the rectifier flux pumps for energizing the field coils of the EcoSwing 3.6 MW HTS wind generator. Both a “warm” solution, with the rectifier at room temperature, and a “cold” solution, in which the latter is integrated into the cryostat, are investigated with regard to the rectifier flux pump. A comparison with the actual, state-of-the-art, system of the EcoSwing machine is carried out in terms of the total required cooling power and the ability to charge the HTS field windi... [more]

A change in thinking has been ongoing in the architecture and building industry in response to growing concern over the role of the building industry in the excessive consumption of energy and its devastating effects on the natural environment. This shift changed the thinking of architects, engineers, and designers in the initial phases of a building’s design, with a change from the importance of geometry and form to assessing a building’s performance, from structure to a building’s skin, and from abstract aesthetics to bio-climatic aesthetics. In this context, sustainable, intelligent, and adaptive building façades were extensively researched and developed. Consequently, several typologies, strategies, and conceptual design frameworks for adaptive façades were developed with the aim of performing certain functions. This study focuses on the biomimetic methodologies developed to design adaptive façades because of their efficiency compared to other typologies. A comprehensive literature... [more]

Buildings account for 40% of the European Union’s energy consumption. Deep energy renovation of residential buildings is key for decarbonization and energy poverty alleviation. However, renovation is occurring at far below the needed pace and depth. In this context, building renovation one-stop shops, which bring all project phases under one roof and provide advice, support, and finance to households, are highlighted as a promising solution. Nevertheless, this model is still absent or under-developed in most European countries and remains understudied in the scientific literature. Therefore, the present research goals are as follows: (i) to provide a critical review of emerging one-stop shop models; (ii) to streamline the deployment of building renovation digital one-stop shops by piloting a sequential multi-staged approach for Portuguese households and proposing it for replication elsewhere; and (iii) to compare case-study insights with other one-stop shops and discuss the notion in t... [more]

The article is a continuation of the authors’ ongoing research related to power flow and voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use Machine Learning (ML) technology in a future grid. Based on supervised learning, a Selectively Coherent Model of Converter System Control for an LV grid (SCM_CSC) is proposed. This represents a fresh, new approach to combining off and on-line computing for DSOs, in line with the decarbonisation process. The main kernel of the model is a neural network developed from the initial prediction results generated by regression analysis. For selected PV system operation scenarios, the LV grid of the future dynamically controls the power flow using AC/DC converter circuits for Battery Energy Storage Systems (BESS). The objective function is to maintain the required voltage conditions for high PV generation in an LV grid line area and to minimise power flows to the MV grid. Based on the training and validation data pr... [more]

The world of electrical distribution is rapidly changing and is seeing more and more distributed production and steerable flexibilities. Energy communities are seen as an important innovation for the optimization of electrical consumption at a local level. A central need of the local energy markets inside energy communities is the exchange and circulation of production and consumption data, and therefore the problem of the potential leak of sensitive data must be addressed. In this paper, the context of the Lugaggia Innovation Community, a Self Consumption Community pilot project in southern Switzerland, is introduced together with the blockchain framework that was created for its internal market interaction and the rules designed for its local energy market. A cryptographic protocol from the literature, based on homomorphic encryption, is then proposed for the anonymous aggregation of production and consumption data of the individual households at a resolution of 15 min. The computati... [more]

Anaerobic digestion (AD), microalgae cultivation, and microbial fuel cells (MFCs) are the major biological processes to convert organic solid wastes and wastewater in the agricultural industry into biofuels, biopower, various biochemical and fertilizer products, and meanwhile, recycle water. Various nanomaterials including nano zero valent irons (nZVIs), metal oxide nanoparticles (NPs), carbon-based and multicompound nanomaterials have been studied to improve the economics and environmental sustainability of those biological processes by increasing their conversion efficiency and the quality of products, and minimizing the negative impacts of hazardous materials in the wastes. This review article presented the structures, functionalities and applications of various nanomaterials that have been studied to improve the performance of AD, microalgae cultivation, and MFCs for recycling and valorizing agricultural solid wastes and wastewater. The review also discussed the methods that have b... [more]

The parametric variation of nonlinear systems remains a significant drawback of automatic system controllers. The Proportional−Integral(PI) and Proportional−Integral−Derivative (PID) are the most commonly used controllers in industrial control systems. However, with the evolution of these systems, such controllers have become insufficient to compete with the complexity of the systems. This problem can be solved with the help of artificial intelligence, and especially with the use of optimization algorithms, which allow for variable gains in PID controllers that adapt to parametric variation. This article presents an analytical and experimental study of the Direct Torque Control (DTC) of a Doubly-Fed Induction Motor (DFIM). The speed adaptation of the DFIM is achieved using a PID controller, which is characterized by overshoots in the speed and ripples in the electromagnetic torque. The Genetic Algorithm (GA) within the DTC shows very good robustness in speed and torque by reducing torq... [more]

Thermal power plants, TPP, are one of the main players in the phosphoric acid and fertilizer production value chain. The control of power plant assets involves considerable complexity and is subject to several constraints, affecting the asset’s reliability and, most importantly, plant operators’ safety. The main focus of this paper is to investigate the potential of an agent-based digital twin architecture for collaborative prognostic of power plants. Based on the ISO 13374:2015 scheme for smart condition monitoring, the proposed architecture consists of a collaborative prognostics system governed by several smart DT agents connected to both physical and virtual environments. In order to apprehend the potential of the developed agent-based architecture, experiments on the architecture are conducted in a real industrial environment. We show throughout the paper that our proposed architecture is robust and reproduces TPP static and dynamic behavior and can contribute to the smart monitor... [more]