Calcined clay, as supplementary cementitious material (SCM), can potentially reduce the cement industry’s carbon footprint by partly avoiding CO2 emissions released from clinker production. Further reduction can be achieved through the electrification of the clay calcination process while coupled with increasing penetration of renewable energy sources (RES). This paper addresses the economic and CO2 emissions performance of an electrified clay calcination process integrated into a reference cement plant. An optimal sizing algorithm is developed to investigate how local renewable-based generation and high-temperature thermal storage can be exploited to improve the economic feasibility of electrification. Results show that even without optimization, the integration allows a reduction of the overall cost of cement for a given geographic area and considering a carbon price of 100 €/tCO2. When applying the optimal sizing algorithm, an additional decrease in costs is observed, primarily due to the lower energy costs achieved by installing a 41.18 MW PV plant and a 340 MWh thermal storage.
Modern heat pump systems often come equipped with sensors, enabling the collection of substantial operational data. However, many residential heat pumps installed in preceding decades lack pressure sensors, energy meters, or mass flow meters, primarily due to financial limitations. As a result of these incomplete measurements, the direct analysis of the heat pump system’s performance or the leveraging of the amassed data for inventive applications like prognosticating energy consumption, detecting and diagnosing faults, and implementing intelligent control becomes challenging.
In existing literature, the focus of soft sensors in heat pump systems has been on estimating a single parameter. This approach, however, overlooks the reality that multiple parameters are often missing due to the lack of all-encompassing physical meters and sensors. Furthermore, current soft sensor models are typically developed using inputs such as compressor power consumption, pressures, evaporation, and condensation temperatures. These inputs, unfortunately, tend to be inaccessible within existing heat pump monitoring installations.
In practice, it is a challenge to compensate for several critical measurements, encompassing mass flow rate, pressures, power consumption, and heating capacity, by using only commonly available sensors such as secondary loop temperatures and compressor frequency are available. Currently, there is a notable gap in research concerning this practical issue.
To address the problems associated with inadequate measurements, this study presents the development and validation of soft sensors based on a data-driven approach, which can compensate for the parameters often unavailable with data collected from a limited number of commonly used sensors. Each component model employs a multivariate polynomial regression that calculates the evaporation temperature, condensation temperature, mass flow rate, and compressor power consumption, respectively. Subsequently, we present an integrated heat pump model that combines these component models into a comprehensive heat pump model.
Finally, we validate the data-driven model against field test installations, demonstrating its accuracy with a relative root mean squared error (RRMSE) ranging from 10% to 20%.
Steam-assisted gravity drainage (SAGD) is one effective and well-established technology for recovering heavy oil and bitumen resources. Extensive research has been conducted on data-driven models to evaluate the production performance of the SAGD process. The artificial neural network (ANN) is a commonly used machine learning method. However, it is crucial to explore other machine learning methods such as Symbolic Regression (SR), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) using field data. In this study, firstly, a data set consisting of thirteen input/output attributes describing production-related properties and production characteristics was extracted from Long Lake field data. Secondly, three different machine learning methods, including Neural Networks (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Symbolic Regression (SR), were employed to establish a relationship between the input and output parameters in the different data sets. Subsequently, a range of models were created, evaluated, and compared. Furthermore, the impact of two feature scaling methods, namely standardization and normalization, on the accuracy of a series of prediction models was explored. Lastly, the sensitivity of the input parameters was analyzed. Analysis of the forecasting results obtained from different models leads to the following conclusions. The study found that standardization and normalization significantly enhance the performance of the artificial neural network model, with standardization being more effective. However, the impact of data scaling on integrated learning models (random forest and extreme gradient boosting tree) is minimal. Interestingly, for models based on symbolic regression algorithms, not using data scaling yields the best results. Both artificial neural network and symbolic regression algorithms demonstrate significant advantages and are suitable for predicting SAGD production. However, the symbolic regression model can derive analytical expressions that describe the input-output relationship, which are easy to interpret and apply. This suggests that symbolic regression algorithms may be preferable to artificial neural network algorithms. The top five factors with the greatest impact on cumulative oil production at the end of the main production stage in SAGD, ranked in descending order of importance, are CSI2 (cumulative steam injection volume at the end of the main production stage), LE (effective length of the horizontal well section), MSIP (mean steam injection pressure during the main production stage), HPVH (height of the hydrocarbon pore volume), and H (effective thickness). The methods proposed in this article are helpful for establishing an intelligent energy management system for SAGD (Steam-Assisted Gravity Drainage) development in heavy oil fields. The system can support decision-making processes by providing accurate forecasting and predictive analytics.
Energy consumption is a recognised global issue, and with the increasing population, there has been a rapid increase in the production of energy to meet the ever-growing demand. However, there is an over-reliance on fossil fuels, which are depletable and cause carbon emission. However, considering the recent international agreements over the need to reduce greenhouse gases that aim to cut greenhouse gas emissions by 50 percent before 2050, it is crucial that research and implementation of Renewable Energy (RE) sources should be made a priority. This study addresses the key stakeholders involved in the supply chain of domestic RE to identify the challenges and opportunities in the implementation of RE in the UK. The methodology involves a survey to customers to identify the reason for implementing, or not, renewable energy technologies in their homes. The results indicate that financial constraints, lack of information, and public acceptance are the main issues that need to be addressed. Therefore, to encourage the adoption of RE, the cost should be subsidised, or addressed via affordable means, to enable consumers to be engaged with clean technologies considering the recent rising costs of electricity.
The need to develop smart and NetZero cities and reduce carbon emission is driving innovation in cities around the world to use electric transportation technologies. Among that the use of e-scooters. Nottingham (UK) is one of the cities that has an e-scooter scheme where people could rent e-scooters to travel around the city. However, in the current situation, to ensure pedestrian safety e-scooters need to be ridden on the road amongst cars, most of them are fossil fuelled. This gives rise to two potential risks for e-scooter users: the air quality that they breathe and the physical risk of being near cars, where drivers may not be familiar with seeing e-scooters on the road. This paper uses a mixed methods approach by conducting surveys to drivers and e-scooter users, jointly with an experimental work to monitor the journey of e-scooter users combining air quality, GPS data and 360 degrees camera footage to assess the risk to e-scooter riders using sensor fusion and artificial intelligence. The results indicate that the suggested novel methodology is effective in understanding the current limitations and the potential air quality and physical risks to e-scooter users.
Carbon dioxide (CO2) contributes to over 50% of the enhanced radiative forcing, which in turn leads to climate change. Regular monitoring of CO2 emissions is commonly required by various governments for strategic management purposes. However, the conventional self-reporting mechanism heavily relies on reporting parties, making it less efficient and subjective. This study proposes a direct method to estimate the CO2 emissions using satellite-based column-averaged mole fractions of CO2 (XCO2) retrievals. To account for spatial and temporal variability, the study adopts the geographically and temporally weighted regression (GTWR) model. The results show high consistency, indicating the potential of using satellite-based data to track anthropogenic emissions with more frequent and extensive coverage.
Recognizing the urgent need for further cost reduction to drive broad adoption of redox flow batteries, it is critical to improve the reactor performance. Improved performance leads to higher efficiency, potential for a decrease of the stack size, and reduced capital cost. As one of the main contributors to reactor internal resistance, porous electrodes with properly designed structures and optimized physicochemical properties offer a pathway to reduced voltage losses, including kinetic and concentration overpotentials. Recently, carbon cloth electrodes were explored in flow battery applications owing to their bimodal pore size distributions, which opens a potential opportunity for improved mass transport behavior. Although the unique woven structure of cloth provides flexibility in the electrode design, finding an optimal trade-off between abundant electrolyte penetration pathways and a high active surface area is still challenging. In the present study, we investigate a dual-layer electrode configuration to meet the requirements of high active surface area and low mass transport resistance. A carbon cloth was placed close to the flow plate to serve as an electrolyte distributor to ensure efficient mass transport in a lateral flow-through configuration, and a carbon paper sub-layer was placed near the membrane to provide a high density of reaction sites. Overall, the results show that the proposed strategy is an effective way to achieve high electrochemical performance and low pressure drop. It can be regarded as a promising approach for boosting system efficiency.
Ammonia production contributes 1% of global carbon emissions due to energy-intensive hydrogen synthesis. To mitigate this, renewable-powered water electrolysis is a promising solution. While numerous studies have explored the use of hydrogen storage and grid backup to align renewable energy sources with the continuous operation of the HB process, recent industry efforts focused on increasing plant flexibility, adjusting the production to intermittent power inputs. In this study, we model and optimize two plant configurationsâ€”continuous and flexible. The aim is to determine the conditions under which flexible production reduces costs. Our results show that renewable resource availability has a significantly greater impact on LCOA than plant configuration. For the same configuration, LCOA differs by a factor of two across regions with low and high-capacity factors. Continuous plants have lower LCOA in solar-dominated regions (-13%). In contrast, flexible plants are more cost-effective in wind-rich regions (-12%) or ones with high-capacity factors for renewable production (-11%) due to their ability to maintain the minimum load with less reliance on batteries or grid imports. The lowest LCOA is 372 EUR/ton NH3 in high-capacity regions, followed by wind-dominated areas (389 EUR/ton NH3), both with flexible configurations.
Liquid metal batteries are considered a competitive alternative for grid-level stationary energy storage. In this study, we investigated the effects of external magnetic fields on the charge and discharge performance of this all-liquid battery composed of three layers of fluids. Experimental results indicate that, at the current density of 500 mA cm-2, the application of a 100 mT magnetic field increases the discharge voltage by 34.64% compared to the case without a magnetic field. At a higher current density of 1000 mA cm-2, applying a 50 mT magnetic field results in a 74.5% increase in discharge voltage, demonstrating significant effects. Furthermore, we develop a numerical model using a multiphysics simulation software to uncover the underlying mechanisms. The Lorentz force generated by the interaction of the external magnetic field and discharge current induces a swirling flow in the phi direction. At sufficiently high flow velocities, turbulent flow with a notable |z| direction component is formed, assisting the transport of Li atoms in the positive electrode, reducing concentration polarization, and thereby enhancing the discharge voltage.
With increasing digitization for constructing intelligent energy systems, automated data processing is moving more and more into focus. Gaps in the recorded data pose a central problem for further processing instances. This work systematically investigates which methods are suitable for the imputation of data gaps of different sizes. It tackles the imputation performanceâ€™s influence on overlying applications, such as load forecasting and total energy determination. The presented method is applied to four datasets of compressors of industrial. Based on these Use Caseâ€™s evaluation results, recommendations for action are derived. Gap sizes should be considered when choosing an imputation method to minimize imputation error. For load forecasting, the prediction error correlates with the imputation error in certain missingness scenarios. Energy consumption analysis on the imputed data yields good results due to a balanced ratio of over- and undershooting of the imputation error.