Water and Energy Monitoring and Their Nexus

Topic Information

Dear Colleagues,

Drought, population growth, energy and land use, socioeconomic changes, and a shifting climate exacerbate the pressure on water and energy infrastructures. Therefore, it is critical to re-think how water and electricity are managed, including their interactions, e.g., water used to generate energy, and energy used in water treatment.

By focusing on the nexus between water and energy, we aim to identify sustainable strategies and technologies that can alleviate the mounting pressures on these vital resources. However, we also recognize the importance of examining water and energy individually to gain a comprehensive understanding of their unique complexities.

In this sense, this topic aims to provide a platform for researchers and participants from academic and industrial sectors to report their recent research findings that contribute to the comprehensive understanding and effective planning and management of water and energy systems. To this end, five journals were meticulously selected: Water, Energies, Sensors, Sustainability, and Data.

We are pleased to invite the research community to submit review or regular research papers focusing on, but not limited to, the following relevant topics related to water and energy monitoring and their nexus:

• Water and energy monitoring technologies;
• Electricity and water disaggregation;
• Water–energy and energy–water nexus modelling;
• Water–energy–food nexus modelling;
• Public datasets;
• Standards for data sharing and re-use;
• Decision-making tools;
• User experience and visualization of the water–energy and energy–water nexus;
• Human factors in water–energy and energy–water nexus;
• Water–energy and energy–water nexus integration in buildings and industries;
• Optimization considering the water–energy and energy–water nexus;
• Integration of RES and DER in energy and water infrastructures and their nexus;
• Forecasting of power production and demand in water–energy and energy–water nexus;
• Forecasting water demand in water–energy and energy–water nexus.

Dr. Lucas Pereira
Dr. Hugo Morais
Dr. Wolf-Gerrit Früh
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Data data	2.2	4.3	2016	27.7 Days	CHF 1600	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600	Submit
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400	Submit
Water water	3.0	5.8	2009	16.5 Days	CHF 2600	Submit

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Published Papers (7 papers)

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All Data Energies Sensors Sustainability Water

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25 pages, 10177 KiB  

Open AccessArticle

Forecasting Gate-Front Water Levels Using a Coupled GRU–TCN–Transformer Model and Permutation Entropy Algorithm

by Jiwei Zhao, Taotao He, Luyao Wang and Yaowen Wang

Water 2024, 16(22), 3310; https://doi.org/10.3390/w16223310 - 18 Nov 2024

Viewed by 458

Abstract

Water level forecasting has significant impacts on transportation, agriculture, and flood control measures. Accurate water level values can enhance the safety and efficiency of water conservancy hub operation scheduling, reduce flood risks, and are essential for ensuring sustainable regional development. Addressing the nonlinearity [...] Read more.

Water level forecasting has significant impacts on transportation, agriculture, and flood control measures. Accurate water level values can enhance the safety and efficiency of water conservancy hub operation scheduling, reduce flood risks, and are essential for ensuring sustainable regional development. Addressing the nonlinearity and non-stationarity characteristics of gate-front water level sequences, this paper introduces a gate-front water level forecasting method based on a GRU–TCN–Transformer coupled model and permutation entropy (PE) algorithm. Firstly, an analysis method combining Singular Spectrum Analysis (SSA) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is used to separate the original water level data into different frequency modal components. The PE algorithm subsequently divides each modal component into sequences of high and low frequencies. The GRU model is applied to predict the high-frequency sequence part, while the TCN–Transformer combination model is used for the low-frequency sequence part. The forecasting from both models are combined to obtain the final water level forecasting value. Multiple evaluation metrics are used to assess the forecasting performance. The findings indicate that the combined GRU–TCN–Transformer model achieves a Mean Absolute Error (MAE) of 0.0154, a Root Mean Square Error (RMSE) of 0.0205, and a Coefficient of Determination (R²) of 0.8076. These metrics indicate that the model outperforms machine learning Support Vector Machine (SVM) models, GRU models, Transformer models, and TCN–Transformer combination models in forecasting performance. The forecasting results have high credibility. This model provides a new reference for improving the accuracy of gate-front water level forecasting and offers significant insights for water resource management and flood prevention, demonstrating promising application prospects. Full article

(This article belongs to the Topic Water and Energy Monitoring and Their Nexus)

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22 pages, 1037 KiB  

Open AccessArticle

Crafting Taxonomies for Understanding Power Consumption in Industrial Kitchens: A Methodological Framework and Real-World Application

by Miriam Ribeiro, Hugo Morais and Lucas Pereira

Sustainability 2024, 16(17), 7639; https://doi.org/10.3390/su16177639 - 3 Sep 2024

Viewed by 603

Abstract

Although industrial kitchens consume significantly more energy than other commercial buildings and represent an important opportunity for sustainable energy systems, researchers have largely overlooked energy efficiency in these spaces. One of the main challenges is the diversity of kitchen configurations, complicating the characterization [...] Read more.

Although industrial kitchens consume significantly more energy than other commercial buildings and represent an important opportunity for sustainable energy systems, researchers have largely overlooked energy efficiency in these spaces. One of the main challenges is the diversity of kitchen configurations, complicating the characterization and generalization of research findings, including establishing a standardized methodology for assessing and benchmarking energy demand. To address this research gap, this paper proposes a methodological framework to develop taxonomies for understanding the electricity consumption in industrial kitchens. The proposed framework was developed following an extensive survey of the existing literature, and it is based on four main steps: identification of the knowledge domain, extraction of terms and concepts, data collection, and information analysis. To demonstrate the proposed framework, a case study was developed involving the participation of 50 restaurants located in Portugal. The proposed framework proved valid as it enabled the construction of a taxonomy that allows the classification of industrial kitchens according to different energy consumption-related concepts, such as costs with energy, the physical size of the kitchen, and the number of workers. Full article

(This article belongs to the Topic Water and Energy Monitoring and Their Nexus)

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18 pages, 1968 KiB  

Open AccessArticle

Trend Prediction and Operation Alarm Model Based on PCA-Based MTL and AM for the Operating Parameters of a Water Pumping Station

by Zhiyu Shao, Xin Mei, Tianyuan Liu, Jingwei Li and Hongru Tang

Sensors 2024, 24(16), 5416; https://doi.org/10.3390/s24165416 - 21 Aug 2024

Viewed by 843

Abstract

In order to effectively predict the changing trend of operating parameters in the pump unit and carry out fault diagnosis and alarm processes, a trend prediction model is proposed in this paper based on PCA-based multi-task learning (MTL) and an attention mechanism (AM). [...] Read more.

In order to effectively predict the changing trend of operating parameters in the pump unit and carry out fault diagnosis and alarm processes, a trend prediction model is proposed in this paper based on PCA-based multi-task learning (MTL) and an attention mechanism (AM). The multi-task learning method based on PCA was used to process the operating data of the pump unit to make full use of the historical data to extract the key common features reflecting the operating state of the pump unit. The attention mechanism (AM) is introduced to dynamically allocate the weight coefficient of common feature mapping for highlighting the key common features and improving the prediction accuracy of the model when predicting the trend of data change for new working conditions. The model is tested with the actual operating data of a pumping station unit, and the calculation results of different models are compared and analyzed. The results show that the introduction of multi-task learning and attention mechanisms can improve the stability and accuracy of the trend prediction model compared with traditional single-task learning and static common feature mapping weights. According to the threshold analysis of the monitoring statistical parameters of the model, a multi-stage alarm model of pump unit operation condition monitoring can be established, which provides a theoretical basis for optimizing operation and maintenance management strategy in the process of pump station management. Full article

(This article belongs to the Topic Water and Energy Monitoring and Their Nexus)

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20 pages, 4426 KiB  

Open AccessArticle

Virtual Inspection System for Pumping Stations with Multimodal Feedback

by Zhiyu Shao, Tianyuan Liu, Jingwei Li and Hongru Tang

Sensors 2024, 24(15), 4932; https://doi.org/10.3390/s24154932 - 30 Jul 2024

Viewed by 806

Abstract

Pumping stations have undergone significant modernization and digitalization in recent decades. However, traditional virtual inspections often prioritize the visual experience and fail to effectively represent the haptic physical properties of devices during inspections, resulting in poor immersion and interactivity. This paper presents a [...] Read more.

Pumping stations have undergone significant modernization and digitalization in recent decades. However, traditional virtual inspections often prioritize the visual experience and fail to effectively represent the haptic physical properties of devices during inspections, resulting in poor immersion and interactivity. This paper presents a novel virtual inspection system for pumping stations, incorporating virtual reality interaction and haptic force feedback technology to enhance immersion and realism. The system leverages a 3D model, crafted in 3Ds Max, to provide immersive visualizations. Multimodal feedback is achieved through a combination of haptic force feedback provided by a haptic device and visual information delivered by a VR headset. The system’s data platform integrates with external databases using Unity3D to display relevant information. The system provides immersive 3D visualizations and realistic force feedback during simulated inspections. We compared this system to a traditional virtual inspection method that demonstrated statistically significant improvements in task completion rates and a reduction in failure rates when using the multimodal feedback approach. This innovative approach holds the potential to enhance inspection safety, efficiency, and effectiveness in the pumping station industry. Full article

(This article belongs to the Topic Water and Energy Monitoring and Their Nexus)

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17 pages, 13057 KiB  

Open AccessArticle

Spatio-Temporal Dynamics of Center Pivot Irrigation Systems in the Brazilian Tropical Savanna (1985–2020)

by Edson Eyji Sano, Ivo Augusto Lopes Magalhães, Lineu Neiva Rodrigues and Édson Luis Bolfe

Water 2024, 16(13), 1897; https://doi.org/10.3390/w16131897 - 2 Jul 2024

Viewed by 1056

Abstract

The 204-million-hectare Brazilian tropical savanna (Cerrado biome), located in the central part of Brazil, constitutes the main region of food and natural fiber production in the country. An important part of this production is based on center pivot irrigation. Existing studies evaluating the [...] Read more.

The 204-million-hectare Brazilian tropical savanna (Cerrado biome), located in the central part of Brazil, constitutes the main region of food and natural fiber production in the country. An important part of this production is based on center pivot irrigation. Existing studies evaluating the spatio-temporal dynamics of center pivots in Brazil do not consider their retraction. This study aimed to evaluate the expansion and retraction of center pivots in the Cerrado biome in the period 1985–2020. We relied on the data produced by the MapBiomas Irriga project. In this period, the area occupied by center pivots increased from 47 thousand hectares in 1985 to 1.2 million hectares in 2020, mostly concentrated in the states of Minas Gerais, Goiás, São Paulo, and Bahia, confirming previous reports available in the literature. Among the 13 irrigation poles recognized by the National Water Agency (ANA), the Oeste Baiano (Bahia State) and the São Marcos (Goiás State) presented the largest areas of center pivots (173,048 ha and 101,725 ha, respectively). We also found that 76% of the center pivots are concentrated in the regions with low water availability (0.01–0.45 mm day⁻¹). Within this 16-year period (2005–2020), more than 10% of center pivots found in 2005 were either abandoned or converted into rain-fed crop production. The results of this study can provide an important foundation for public policies directed toward the sustainable use of water resources by different consumers. Full article

(This article belongs to the Topic Water and Energy Monitoring and Their Nexus)

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20 pages, 2664 KiB  

Open AccessArticle

Is Biomethane Production from Common Reed Biomass Influenced by the Hydraulic Parameters of Treatment Wetlands?

by Liviana Sciuto, Feliciana Licciardello, Antonio Carlo Barbera, Vincenzo Scavera, Salvatore Musumeci, Massimiliano Severino and Giuseppe Luigi Cirelli

Sustainability 2024, 16(7), 2751; https://doi.org/10.3390/su16072751 - 26 Mar 2024

Viewed by 818

Abstract

Treatment wetlands (TWs) are Nature-Based Solutions which have been increasingly used worldwide for wastewater (WW) treatment as they are able to remove mineral and organic pollutants through both physical and biochemical processes. Besides the reusable effluent, the TWs produce, as their main output, [...] Read more.

Treatment wetlands (TWs) are Nature-Based Solutions which have been increasingly used worldwide for wastewater (WW) treatment as they are able to remove mineral and organic pollutants through both physical and biochemical processes. Besides the reusable effluent, the TWs produce, as their main output, plant biomass that needs to be harvested and disposed of at least once a year with significant management costs and causing the TW to be temporarily out of service. This study aims (i) to evaluate the potential of TWs’ biomass for local energy production and (ii) to understand the effects of TWs’ hydraulic conductivity (Ks) on the biomass biomethane yield. Specifically, this was addressed by determining the Biochemical Methane Potential of common reed (CR) (Phragmites australis) samples collected at three harvest times from the 10-year-old horizontal subsurface treatment wetland (HSTW) used as a secondary WW treatment system for the IKEA^® store situated in Catania (Eastern Sicily, Italy). Furthermore, the falling-head test was conducted to assess the hydraulic conductivity (Ks) variation in the hydraulic conductivity (Ks) of the HSTW, in order to understand its influence on the CR biomethane production. The average methane content values were 130.57 Nm³CH₄/tVS (±24.29), 212.70 Nm³CH₄/tVS (±50.62) and 72.83 Nm³CH₄/tVS (±23.19) in August, September, October 2022, respectively. Ks was correlated with both dry matter (R² = 0.58) and fiber content (R² = 0.74) and, consequently, affected the biomethane yield, which increased as the Ks increased (R² = 0.30 in August; R² = 0.57 in September). In the framework of a circular economy, the results showed the successful possibility of integrating bioenergy production into TWs. The research could contribute (i) to encouraging plant operators to reuse biomass from TWs for local energy production and (ii) to help plant operators to understand Ks effects on the biomass biomethane yield in order to increase the sustainability of the system and to reduce the maintenance costs. Full article

(This article belongs to the Topic Water and Energy Monitoring and Their Nexus)

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16 pages, 2973 KiB  

Open AccessArticle

Phytohormone Supplementation for Nutrient Removal from Mariculture Wastewater by Oocystis borgei in Sequential Batch Operation

by Yang Liu, Chengcheng Deng, Xinyue Song, Zhangxi Hu, Feng Li, Yulei Zhang, Changling Li, Xianghu Huang and Ning Zhang

Water 2024, 16(4), 552; https://doi.org/10.3390/w16040552 - 11 Feb 2024

Cited by 2 | Viewed by 1825

Abstract

To enhance the nutrient removal efficiency of Oocystis borgei for mariculture wastewater (MW), the effects and processes of three phytohormones on nitrogen and phosphorus removal from synthetic mariculture wastewater (SMW) by O. borgei under sequential batch operation were compared. The findings revealed that [...] Read more.

To enhance the nutrient removal efficiency of Oocystis borgei for mariculture wastewater (MW), the effects and processes of three phytohormones on nitrogen and phosphorus removal from synthetic mariculture wastewater (SMW) by O. borgei under sequential batch operation were compared. The findings revealed that the supplementation with 10⁻⁶ M 3-indoleacetic acid (IAA), gibberellic acid (GA3), and zeatin (ZT) resulted in the most effective elimination, while there was no appreciable difference among them. The nitrogen and phosphorus indices of the effluent dramatically reduced (p < 0.01) upon the supplementation of phytohormones, and the removal effects were ranked as NO₃⁻-N > PO₄³⁻-P > NH₄⁺-N > NO₂⁻-N. The removal rates for NH₄⁺-N and PO₄³⁻-P were 0.72–0.74 mg·L⁻¹·d⁻¹ and 1.26–1.30 mg·L⁻¹·d⁻¹, respectively. According to physiological studies, phytohormones enhanced the levels of photosynthetic pigments and chlorophyll fluorescence parameters (Fv/Fm and φPSII), thereby improving photosynthetic activity. Additionally, they stimulated Nitrate Reductase (NR) and Glutamine Synthetase (GS) activities to promote nitrogen metabolism and increased Superoxide Dismutase (SOD), Catalase (CAT), and carotenoid contents to mitigate oxidative stress damage caused by abiotic stress. These activities contribute to the proliferation of O. borgei, which in turn resulted in an increase in the assimilation of nitrogen and phosphorus from SMW. In conclusion, phytohormone supplementation significantly increased nutrient removal from SMW by O. borgei in a sequential batch reactor, which has potential application in MW treatment. Full article

(This article belongs to the Topic Water and Energy Monitoring and Their Nexus)

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