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The U.S. Environmental Protection Agency’s (EPA) Superfund—the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) database—has collected and built an open-source database based on nearly 2000 US soil remediation cases since 1980, providing detailed information and references for researchers worldwide to carry out remediation work. However, the cases were relatively independent to each other, so the whole database lacks systematicness and instructiveness to some extent. In this study, the basic features of all 144 soil remediation projects in four major oil-producing states (California, Texas, Oklahoma and Alaska) were extracted from the CERCLA database and the correlations among the pollutant species, pollutant site characteristics and selection of remediation methods were analyzed using traditional and machine learning techniques. The Decision Tree Classifier was selected as the machine learning model. The results showed that the growth of new contaminated sites has slowed down in recent years; physical remediation was the most commonly used method, and the probability of its application is more than 80%. The presence of benzene, toluene, ethylbenzene and xylene (BTEX) substances and the geographical location of the site were the two most influential factors in the choice of remediation method for a specific site; the maximum weights of these two features reaches 0.304 and 0.288.

Energy efficiency is an increasingly important dimension of household appliances, which is why they are labeled to indicate their energy consumption. In 2020, the European Union countries changed the labeling system from the previous system: ranging from A+++ to D, to the new system: ranging from A to G, assuming it would be more transparent for the consumer. The aim of the study was to find out the extent to which consumers are aware of the new labeling system, and the impact that the new labels have (compared to the previous ones) on the perception of household appliances and consumer decision-making. For this purpose, the survey was conducted on a nationwide representative Polish sample (n = 1054). The research was partly experimental, where the same appliances were presented with new and previous energy labels. The results showed that currently most people do not identify the new energy classes. Furthermore, products with the new labels are perceived as being less energy efficient in comparison with products with the previous labels, which shows that there is some confusion among consumers in terms of the new energy efficiency labeling system.

Nuclear power plants in the United States are increasingly challenged to compete in wholesale electricity markets due to the low electricity costs and increasingly dynamic grid conditions from competing generation sources. An alternative market for nuclear power is industrial facilities that can use the thermal and/or electrical power generated by a nuclear power plant to offset the economic losses incurred by electricity market challenges. A generic pressurized water reactor (PWR) simulator was used to show the results of a basic design for a generic thermal power extraction system and tests were run using a set of procedures to show what happens when a nuclear power plant transitions from full electrical power dispatch to 15% and 50% thermal power dispatch. This type of operation leads to losses in turbine performance efficiency due to the deviation from the design operating point, but because the thermal power is also used by the industry load without conversion losses, the combined thermal efficiency of the PWR increases. For the 15% case, the thermal efficiency increased from 32% to 41.9%, while for the 50% case, the efficiency increased up to 60.1%. In addition, for 50% thermal power dispatch, the power dissipated by the condenser decreased from approximately 2000 to approximately 1300 MW (thermal), indicating a substantially diminished impact on the environment in terms of releasing heat into the cooling water reservoir.

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system.

The occurrence of surface discontinuous deformations in post-mining areas is currently a significant and important problem, due to both the frequency of their occurrence and the threat they pose to public safety. This paper presents the results of research concerning the possibility of sinkhole formation in the areas of abandoned mining excavations. For the purpose of assessing the condition of the rock mass disturbed by the existence of numerous mining excavations, electrical resistivity tomography investigations were carried out for the selected area where mining was undertaken in the past at shallow depths and many underground workings accessing the deposit exist. The sinkhole hazard was also analysed theoretically with a new original model based on the solution of A. Sałustowicz’s pressure arch theory.

This paper endeavors to apply a novel intelligent damping controller (NIDC) for the static synchronous compensator (STATCOM) to reduce the power fluctuations, voltage support and damping in a hybrid power multi-system. In this paper, we discuss the integration of an offshore wind farm (OWF) and a seashore wave power farm (SWPF) via a high-voltage, alternating current (HVAC) electric power transmission line that connects the STATCOM and the 12-bus hybrid power multi-system. The hybrid multi-system consists of a battery energy storage system (BESS) and a micro-turbine generation (MTG). The proposed NIDC consists of a designed proportional–integral–derivative (PID) linear controller, an adaptive critic network and a proposed functional link-based novel recurrent fuzzy neural network (FLNRFNN). Test results show that the proposed controller can achieve better damping characteristics and effectively stabilize the network under unstable conditions.

As a major technical route to utilize biomass energy, biomass combustion power generation (BCPG) has been shown to be of environmental and economic significance. According to the operating experience, the installed capacity has a decisive impact on the operation and economic return of BCPG projects. In China, an installed capacity of either 30 MW or 12 MW is often chosen for constructing a BCPG project. To explore which one is more suitable for China, this paper uses actual operating data to compare the operation performance and techno-economics of two representative BCPG projects with an installed capacity of 30 MW and 12 MW. The results show that the operation situation and electricity production of the 30 MW project are better than those of the 12 MW project. The 30 MW project has a lower biomass consumption than the 12 MW project to produce per unit of electricity. The Internal Rate of Return (IRR) of the 30 MW project is greater than the industry benchmark in China and is almost three times the IRR of the 12 MW project. Therefore, it is recommended to construct BCPG projects with installed capacity of 30 MW in China.

In this paper, recent achievements in the application of the lattice Boltzmann method (LBM) to complex fluid flows are reported. More specifically, we focus on flows through reactive porous media, such as the flow through the substrate of a selective catalytic reactor (SCR) for the reduction of gaseous pollutants in the automotive field; pulsed-flow analysis through heterogeneous catalyst architectures; and transport and electro-chemical phenomena in microbial fuel cells (MFC) for novel waste-to-energy applications. To the authors’ knowledge, this is the first known application of LBM modeling to the study of MFCs, which represents by itself a highly innovative and challenging research area. The results discussed here essentially confirm the capabilities of the LBM approach as a flexible and accurate computational tool for the simulation of complex multi-physics phenomena of scientific and technological interest, across physical scales.