Sustainable Production Systems Research Articles

Bayesian Evaluation of Growth Rates and Kleiber's Ratios in Harnali Sheep: Dissecting Maternal and Additive Genetic Contributions.

Understanding the genetic basis of growth and metabolic traits in sheep is crucial for improving production efficiency and sustainability. The current study aimed to estimate the genetic influences, both direct and maternal, on growth rate and Kleiber's ratio traits in Harnali sheep using pedigree data under Bayesian inference. The data pertained to 2404 animals spanned over 24 years (1998-2021). Fixed factors such as birth period, lamb sex and dam's weight at lambing were considered. The traits studied included average daily gains (ADGs) categorised into ADG1 (birth to weaning age), ADG2 (weaning to 6 months of age) and ADG3 (6-12 months of age), as well as corresponding Kleiber's ratios (KR1, KR2 and KR3). Six single-trait animal models were employed to estimate covariance components and heritabilities, integrating direct additive and maternal effects alongside significant fixed factors using THRGIBBS1F90 and POSTGIBBSF90 programmes. Direct heritability estimates were obtained for ADG1 (0.11 ± 0.05), ADG2 (0.06 ± 0.03), ADG3 (0.03 ± 0.03), KR1 (0.07 ± 0.03), KR2 (0.06 ± 0.03) and KR3 (0.05 ± 0.03). Maternal genetic effects have contributed significant particularly to pre-weaning traits. The study identified an antagonistic relationship between direct additive and maternal genetic effects. Positive genetic and phenotypic correlations emphasised the intricate relationship between growth and metabolic efficiency in Harnali sheep. The current study offers critical insights into the genetic basis of growth and metabolic traits in Harnali sheep, ultimately contributing to more efficient and sustainable sheep production systems.

Evaluation of Soil Tillage and Direct Sowing Systems for Sustainable Production in Tokat Kazova in Terms of Sowing Quality

Evaluation of Soil Tillage and Direct Sowing Systems for Sustainable Production in Tokat Kazova in Terms of Sowing Quality

The Rumen Microbiome Composition of Raramuri Criollo and European Cattle in an Extensive System

Understanding the relationship between Raramuri Criollo cattle (RC) and their microbial ruminal ecosystem will help identify advantageous characteristics of adapted cattle as alternatives to achieve sustainable beef production systems. Our objective was to characterize the rumen microbiome of RC in comparison to Angus and Hereford breeds (European, E) and the cross between them (E × RC). Ruminal fluid was collected from 63 cows in their second productive cycle after grazing in the same paddock for 45 d, in the dry (n = 28) and rain (n = 35) seasons. DNA from ruminal fluid was isolated for 16s rRNA gene next-generation sequencing. The data were analyzed with QIIME2 and compared against the SILVA 16s rRNA database. Beta diversity was different (p < 0.05) between RC and E in both seasons. A microbial core was represented by the most abundant phyla. Planctomycetes and Spirochaetes represented above 1% in the rain season and below 1% in the dry one, whereas Euryarchaeota was below 1% and around 3%, respectively. LEfSe analysis identified differentiated (p < 0.05) key microbial groups that explain the differences between lineages at different taxonomic levels, reflecting the ability of the rumen ecosystem of RC cattle to adapt to hostile environmental conditions by having microbial groups specialized in the degradation of highly fibrous content.

AI-driven optimization of agricultural water management for enhanced sustainability

Optimizing agricultural water resource management is crucial for food production, as effective water management can significantly improve irrigation efficiency and crop yields. Currently, precise agricultural water demand forecasting and management have become key research focuses; however, existing methods often fail to capture complex spatial and temporal dependencies. To address this, we propose a novel deep learning framework that combines remote sensing technology with the UNet-ConvLSTM (UCL) model to effectively integrate spatial and temporal features from MODIS and GLDAS datasets. Our model leverages the high-resolution spatial data from UNet and the temporal dependencies captured by ConvLSTM to significantly improve prediction accuracy. Experimental results demonstrate that our UCL model achieves the best R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} compared to existing methods, reaching 0.927 on the MODIS dataset and 0.935 on the GLDAS dataset. This approach highlights the potential of AI and remote sensing technologies in addressing critical challenges in agricultural water management, contributing to more sustainable and efficient food production systems.

Organic cropping systems balance environmental impacts and agricultural production

Agriculture provides food to a still growing population but is a major driver of the acceleration of global nutrient flows, climate change, and biodiversity loss. Policies such as the European Farm2Fork strategy aim to mitigate the environmental impact of land-use by fostering organic farming. To assess long-term environmental impact of organic food production we synthesized more than four decades of research on agronomic and environmental performance of the oldest system comparison experiment on organic and conventional cropping systems (DOK experiment). Two organic systems (bioorganic and biodynamic) are compared with two conventional (manure-based integrated and mineral-based) systems all with the same arable crop rotation including grass-clover, and manure from livestock integrated in all except the mineral-based system. Organic systems used 92% less pesticides and 76% less mineral nitrogen than conventional systems. Nitrogen use efficiency, that also considers biological nitrogen fixation, was above 85% for all systems. Organic fertilization with farmyard manure maintained or increased soil carbon and nitrogen stocks in the long term, especially in the biodynamic system with manure compost. Conventional mineral-based cropping reduced soil organic carbon and nitrogen stocks. Higher soil organic carbon stocks in organic cropping did not translate to increased N2O emissions, which were the main driver for 56% lower soil-based, area-scaled climate impact compared to the integrated conventional system with manure. Organic cropping systems, especially compost-based biodynamic, showed enhanced soil health, richness of micro- and macrofauna and weed species. Highest yields were achieved in integrated conventional system, with highest total nitrogen inputs and enhanced soil health compared to pure mineral fertilization. Yet, these benefits come at the cost of lower nitrogen use efficiency and higher N2O emissions. Despite a rigorous reduction of inputs yields of the organic systems achieved 85% of the conventional systems. We demonstrate at field level that organic cropping systems with reduced external nutrient inputs have less climate impact and a larger in-situ biodiversity, while providing a fertile ground for the future development of sustainable agricultural production systems.

A Fuzzy Logic Approach to Performance Evaluation of Genomics-Driven Crop Improvement

Genomics-driven crop improvement has emerged as a revolutionary approach to enhance yield, resilience, and nutritional value in agriculture. However, traditional performance evaluation methods often must catch up with agricultural systems' complex, multifaceted nature. This study proposes a fuzzy logic-based framework to assess the performance of genomics-driven crop improvement initiatives. By integrating fuzzy logic, we can effectively manage uncertainty and ambiguity inherent in agricultural data, allowing for a more nuanced evaluation of crop traits, environmental factors, and management practices. The paper begins by elucidating the fundamental principles of genomics-based agriculture and its impact on crop development. It then delves into the theoretical underpinnings of fuzzy logic and its applicability in modeling the multifaceted aspects of crop growth, encompassing genetic, environmental, and physiological factors. Through a synthesis of existing research and empirical data, the paper illustrates the effectiveness of fuzzy logic in capturing the nuances of crop development processes, including germination, flowering, and yield formation. This paper underscores the pivotal role of fuzzy logic analysis in advancing genomics-based agriculture, offering insights into the dynamic interactions shaping crop development and facilitating informed decision-making for sustainable and resilient food production systems in the face of evolving environmental challenges. Results indicate that the fuzzy logic approach enhances the accuracy of performance assessments and facilitates better decision-making in crop management.

Evaluating Maize Residue Cover Using Machine Learning and Remote Sensing in the Meadow Soil Region of Northeast China

The management of crop residues in farmland is crucial for increasing soil organic matter and reducing soil erosion. Identifying the regional extent of crop residue cover (CRC) is vital for implementing conservation tillage and formulating agricultural subsidy policies. The Google Earth Engine (GEE) and remote sensing images from 2019 to 2023 were used to obtain spectral characteristics before the maize seedling stage in Northeast China, followed by constructing the CRC estimation models using machine learning algorithms. To avoid the impact of multicollinearity among data, three machine learning algorithms—ridge regression (RR), partial least squares regression (PLSR), and least absolute shrinkage and selection operator (LASSO)—were employed. By comparing the accuracy of these methods, the most accurate model was determined and applied to subsequent CRC estimation. Based on the estimated CRC and Conservation Technology Information Center definitions of tillage practices, the conservation tillage mapping was completed, and the spatiotemporal distribution characteristics were thoroughly analyzed. The following findings were demonstrated: (1) the PLSR-based model outperformed RR (Pearson’s correlation coefficient (r) = 0.8875, R2 = 0.7877, RMSE = 6.99%) and LASSO (r = 0.8903, R2 = 0.7926, RMSE = 6.88%) with higher accuracy (r = 0.9264, R2 = 0.8582, RMSE = 4.93%). (2) Over the five years, the average no-tillage (NT) proportion in the study area was 15.9%, reduced tillage (RT) was 17.8%, and conventional tillage (CT) was 66.3%. In 2020 and 2022, NT rates were significantly higher at 27.5% and 15.5%, while RT were 15.7% and 30.0%, respectively. (3) Compared to the Sanjiang and Liaohe Plains (RT = 1907 km2 and 1336 km2, and NT = 559 km2 and 585 km2, respectively), the Songnen Plain exhibited higher conservation tillage rates (where RT was 3791 km2 and NT was 1265 km2). This provides crucial scientific evidence for the management and planning of conservation tillage, thereby optimizing farmland production planning, enhancing production efficiency, and promoting the development of sustainable agricultural production systems.

Innovative Tillage Practices to Establish Productive and Sustainable Forage Production Systems in Degraded Alfalfa Pastures in Semiarid Regions

ABSTRACTTillage management practices play a critical role in maintaining sustainable forage production systems in degraded alfalfa (Medicago sativa L.) pastures. However, climate change leads to the precipitation exhibits greater variability, the impacts of tillage practices on alfalfa productivity and forage quality as well as the relationships between water consumption and soil properties remain poorly understood. The field trial conducted from 2018 to 2020 aimed to investigate the impacts of different tillage treatments (i.e., no tillage [CK], strip subsoiling tillage [ST], strip rotary tillage [RT], and strip rotary tillage after strip subsoiling [SRT]) on soil properties, water use efficiency (WUE), and forage productivity. Compared with CK, tillage practices significantly increased forage biomass and crude protein yield (CPY), particularly in the case of the SRT treatment, in which the increase was 20.8% and 25.3% higher, respectively (p < 0.05). Meanwhile, the net income in the SRT treatment exhibited the greatest value (US $1922.0 ha−1), showing a 17.0% increase compared with that in the CK treatment. The relationship of soil structure and crop water consumption became evident when tillage practices were applied in the degraded alfalfa pasture. The soil bulk density under the ST, RT, and SRT treatments decreased by 6.9%, 5.4%, and 8.6%, whereas the soil porosity increased by 8.6%, 6.7%, and 10.7% in the 0–60 cm soil layer, respectively (p < 0.05). Furthermore, the soil water content at a depth of 0–60 cm increased by 12.1% in the SRT treatment compared with the CK treatment at the harvesting stage, and the SRT treatment obtained the highest WUE of dry matter (16.9 kg ha−1 mm−1) and precipitation use efficiency of dry matter (15.8 kg ha−1 mm−1). In addition, total nitrogen in the ST, RT, and SRT treatments increased significantly by 21.4%, 11.1%, and 27.0%, respectively, and soil organic carbon in the ST, RT, and SRT treatments also increased significantly by 5.5%, 3.3%, and 10.4%, respectively, compared with those in the CK treatment (p < 0.05). Our study has demonstrated that tillage practices can optimize soil structure, improve water utilization, and increase soil fertilizer for enhancing forage productivity. The SRT practice could be a preferable approach for sustainable agricultural production of degraded alfalfa grassland in the semiarid region of China.

Photocatalytic Ammonia Decomposition Using Dye-Encapsulated Single-Walled Carbon Nanotubes

The photocatalytic decomposition of ammonia to produce N2 and H2 was achieved using single-walled carbon nanotube (SWCNT) nanohybrids. The physical modification of ferrocene-dye-encapsulated CNTs by amphiphilic C60-dendron yielded nanohybrids with a dye/CNT/C60 coaxial heterojunction. Upon irradiation with visible light, an aqueous solution of NH3 and dye@CNT/C60-dendron nanohybrids produced both N2 and H2 in a stoichiometric ratio of 1/3. The action spectra of this reaction clearly demonstrated that the encapsulated dye acted as the photosensitizer, exhibiting an apparent quantum yield (AQY) of 0.22% at 510 nm (the λmax of the dye). This study reports the first example of dye-sensitized ammonia decomposition and provides a new avenue for developing efficient and sustainable photocatalytic hydrogen production systems.

Development of a geothermal-driven multi-output scheme for electricity, cooling, and hydrogen production: Techno-economic assessment and genetic algorithm-based optimization

This study aims to develop an environmentally friendly multi-energy system for sustainable production of electricity, cooling and hydrogen. The study introduces a pioneering geothermal-based system, integrating an ejector refrigeration cycle, a dual-loop organic Rankine cycle, and a hydrogen production unit with proton exchange membrane electrolyzers. The study provides a thorough analysis of the system's energy and exergy performance, as well as its economic feasibility. Through sensitivity and parametric analyses, the research identifies key parameters that significantly influence system performance. The system's innovative design promises minimal environmental impact while delivering multifaceted performance: generating 1.38 MW of electricity, supplying 436 kW of cooling load, and producing 5.39 kg/h of hydrogen. In the exergy analysis, Evaporator1 is identified as the primary contributor to exergy loss, representing 34 % of the total exergy destruction. This is followed by the electrolysis unit, the condenser, and the ejector refrigeration cycle, which contribute 18 %, 14 %, and 12 %, respectively. The system achieves optimal efficiency at an organic Rankine cycle turbine1 inlet temperature of 387 K, yielding a power generation of 885.4 kW and an exergy efficiency of 26.7 %. Beyond this temperature, any further increase leads to a decline in power output due to operational disturbances. A multi-criteria optimization using genetic algorithm is applied, resulting in an optimized system with a cost rate of 18.13 $/h and an exergy efficiency of 38.96 %.

Unveiling the Genetic Architecture of Semen Traits in Thai Native Roosters: A Comprehensive Analysis Using Random Regression and Spline Function Models.

Improving reproductive traits, particularly semen quality and quantity, is crucial for optimizing poultry production and addressing the current limitations in native chicken reproduction. The aim of this study was to develop a genetic model to estimate genetic parameters guiding the selection of individual Thai native roosters. Using data collected from 3475 records of 242 Thai native grandparent roosters aged 1-4 years, we evaluated semen traits (mass movement, semen volume, and sperm concentration) over 54 weeks. A random regression test-day model incorporating five covariance functions, including a linear spline function with four, five, six, and eight knots (SP4, SP5, SP6, and SP8) and second-order Legendre polynomial function (LG2), was used to estimate genetic parameters. The results showed that the SP8 model consistently outperformed the other models across all traits, with the lowest mean square error, highest coefficient of determination, and superior predictive ability. Heritability estimates for mass movement, semen volume, and sperm concentration ranged from 0.10 to 0.25, 0.22 to 0.25, and 0.11 to 0.24, respectively, indicating moderate genetic influence on these traits. Genetic correlations between semen volume and sperm concentration were highest in the SP8 model, highlighting a strong genetic association between these traits. The SP8 model also revealed a high genetic correlation between mass movement and semen volume, supporting the potential for selecting mass movement as a predictor of semen volume. In conclusion, this study highlights the effectiveness of random regression models with linear spline functions to evaluate the genetic parameters of semen traits in native Thai roosters. The SP8 model is a robust tool for breeders to enhance the reproductive performance of native Thai chickens, contributing to sustainable poultry production systems.

Near-Infrared Spectroscopy in Animal Nutrition: Historical Insights, Technical Principles, and Practical Applications

Optimizing forage quality is vital for enhancing animal performance and supporting the global animal production industry. Near-infrared (NIR) spectroscopy offers a rapid, non-destructive alternative to traditional, time-intensive laboratory analyses, enabling the on-site assessment of forage properties with significant advantages in cost, speed, and environmental impact. This review traces the development of NIR spectroscopy, outlines its core principles, and highlights its applications in animal nutrition. Additionally, it discusses the current technological state, challenges, and future prospects, emphasizing NIR’s growing role in promoting more sustainable and efficient animal production systems.

Towards better understanding the economic and environmental sustainability of alternative agricultural cropping production systems through integrated modelling

We explore and develop an integrated model to enhance understanding and evaluation of the economic and environmental sustainability performance of alternative agricultural cropping production systems. More specifically, our developed Environmentally Sustainable Residual Income Decomposition Modelling (ESRIDM) integrates accounting decomposition analysis, Water and Economic Sustainability Performance Measurement (WESM), system thinking, modular thinking, Environmentally Sustainable Residual Income (ESRI) theory, into a holistic modelling system influenced by Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) approaches.The ESRIDM is theorised and applied in the context of nitrogen management in Australian Cotton production through a detailed case study and comparative analysis of three nitrogen fertiliser options. We find the ESRIDM is able to: (i) provide an overall assessment of the economic and environmental sustainability performance of each scenario modelled; (ii) increase the transparency of the production system to enable the identification of key issues which influence the likely suitability of each scenario; (iii) enable an analysis of probable outcomes under various alternatives; (iv) evaluate the feasibility of regulatory option modelled. While this paper offers a novel perspective to a specific context, ESRIDM is designed to be adapted to a variety of contexts with a view to contributing to the evolution of more sustainable systems of production more generally.

A thermodynamic approach to evaluating the ecological health and sustainability of integrated production systems in Goharkuh Taftan agro-industrial complex and sensitivity analysis of the results

In recent times, the increasing influx of energy inputs into farming systems has led to a significant enhancement in their overall efficiency. However, this has happened at the expense of endangering the sustainability of these systems and degrading the environment. Therefore, it is crucial to develop a methodology to evaluate the resilience of agricultural systems. This study utilised the Steinborn and Svirezhev methodology to assess five different production systems (wheat, barley, alfalfa, cotton, and Pistachio) within the Goharkuh Taftan agro-industrial complex. The approach measures the excessive production of entropy, which acts as an indicator of the system's departure from sustainability. The study focuses on four components: overproduction of entropy, limit energy load, maximum crop yield for sustainable agriculture, and deviation from sustainable agriculture. The results indicated that the production systems analysed in this study produce surplus entropy, thus rendering them in an unstable condition. Among all the products, alfalfa had the lowest entropy overproduction, while pistachio had the highest. The three agricultural commodities, namely wheat, barley, and cotton, are situated at a point equidistant from the two opposite ends. Alfalfa has shown greater energy use efficiency compared to pistachios. It surpasses the maximum crop yield for sustainable agriculture and has less deviation from sustainable agriculture than other integrated production systems. The differences in the intensity of energy flow and the structural characteristics of the integrated production systems were responsible for the variations in the values of the examined components. Nevertheless, none of these solutions are sustainable in the long term. An analysis of the energy inputs and components of the harvest index revealed the importance of implementing management techniques that decrease the intensity of energy flows into these systems and enhance the harvest index to attain a sustainable state. Integrating supplementary renewable energy sources will bolster the long-term sustainability of production systems.

Building a system of indicators of stability and sustainability of industrial enterprise production system functioning

Subject. The article deals with the development of a system of indicators of stability and sustainability of production system functioning at industrial enterprises. Objectives. The aim is to develop a methodological approach to the formation of a system of indicators for operational assessment of production system stability, and sustainability of its functioning. Methods. We employ methods of analyzing the existing approaches to assessment of stability and sustainability of enterprise production system, their generalization, and synthesis of a system of indicators characterizing the impact of the most critical production risks on the production system. Results. The paper offers a methodological approach to the formation of the said system of indicators, underpins the need to assess stability and sustainability of production system functioning, introduces an indicator of stability reserve of production system functioning that enables to assess the level of stability of production system operation. Conclusions. The proposed methodological approach makes it possible to develop management solutions aimed at restoring the stability of the functioning of an industrial enterprise.

Livestock production systems in wetlands of Argentina: assessing transition toward sustainable agroecological systems-

ABSTRACT The magnitude of the current environmental degradation requires an urgent restructuring of food production systems. In this sense, agroecology is considered a sustainable approach, because it provides tools to guide transition toward sustainable production systems and to meet the Sustainable Development Goals (SDG). Nevertheless, it is imperative to possess evidence regarding the implementation of agroecology and current extent. The goals of this study are to compare two instruments for assessing agroecological transitions, to measure the agroecological performance of extensive livestock systems in slightly disturbed wetlands, identify agroecological elements that differentiate farm categories, and identify relationships among these elements. We evaluated 19 representative extensive livestock production ranches from central-western Argentina. We used the Characterization of Agroecological Transition (CAET) from the Tool for Agroecology Performance Evaluation (TAPE). Moreover, CAET was modified through interaction with ranchers and technicians (Adapted CAET). The results obtained with both tools revealed that the agroecological performance of extensive livestock systems is currently in transition, with an overall average of 56.9%. This suggests that there is a group of farmers implementing agroecological strategies which have the potential to drive the transition to achieve the SDGs. This research provides valuable knowledge for applying agroecology principles in minimally impacted wetlands.

Soybean biorefinery and technological forecasts based on a bibliometric analysis and network mapping

Global warming and increasing pollution have become critical global issues, and, coupled depletion of traditional energy sources, have accelerated the adoption of more sustainable production systems, such as biorefineries. Biorefineries possess the capacity to integrate technologies and processes within a single facility, thereby generating biofuels, high-value-added chemical products, and energy. The current study aims to evaluate various scenarios of soybean biorefineries by employing a bibliometric analysis and network mapping to facilitate and implement technological forecasting. To achieve this objective, eight methodological steps were undertaken. The majority of studies reviewed were primarily concerned with the utilization of soybean straw and hulls, motivated by the need to address environmental challenges related to the disposal of substantial volumes of these residues. Research on the application of soy whey also emerged as significant, mainly due to its connection with soy protein isolates. The seven most promising technological avenues identified were nanocomposites, peroxidases, ethanol, green composites, biochar, films, and biodiesel. Consequently, the findings provide a bibliographic foundation for future research on the integration of soybean-derived residues, processes, and products, which could foster innovation within the biorefinery framework and lead to crucial advancements in the processes.

Integrating life cycle assessment and multi criteria decision making analysis towards sustainable cocoa production system in Indonesia: An environmental, economic, and social impact perspective

Sustainable Cocoa production practices should be investigated comprehensively to address sustainability requirements and mitigate Cocoa production issues in Indonesia. This study aims to identify the sustainable Cocoa production system considering the environmental, economic, and social impacts. Life cycle framework and multicriteria decision-making (MCDM) were integrated to obtain the study's objectives by comparing Cocoa monocropping system (CM) and Cocoa intercropping systems (IC). The result indicated that in the environmental sustainability aspect, the monocropping system (CM) showed higher performance as indicated by the lower environmental impact in all indicators; for example, CM emitted a lower Global Warming Potential (GWP) that has a lower margin of 34.5–55.9 % compared to the intercropping system (IC-I and IC-II). In the economic aspect, both on the short-term and long-term analysis, the Cocoa intercropping system (IC-II) generated higher value-added and economic feasibility, with a higher profit margin of 150–205 % compared to CM and IC-1. Along with the increase of economic benefits in IC II, this system also significantly provides social benefits, as presented by the higher social index margin of 4.9–23.7 % compared to other systems. Furthermore, by applying decision-making analysis, the result determines the highest index on the Cocoa intercropping system II (IC-II). These findings highlight that applying the intercropping system II is recommended to overcome the cocoa issue at the farmer and decision-maker levels. Additionally, the proposed method that combined LCA-MCDM can be applied to another agricultural commodity to achieve sustainable agriculture production.

Energy and exergy analysis of a novel solar-powered passive multi-stage osmosis desalination system for sustainable water production

Climate change and population growth constantly exacerbate global water scarcity, and seawater desalination technology is crucial to addressing this crisis. Conventional fossil-fuel desalination poses environmental and economic challenges, especially in remote, water-scarce regions. Renewable energy offers sustainable options for seawater desalination; however, most related technologies face challenges in flexible deployment. Among passive technologies, interfacial evaporation still confronts issues of salt accumulation impacting desalination efficiency. Therefore, this study investigates a novel solar-powered passive multi-stage osmosis device that emulates mangroves’ transpiration and natural osmosis processes. Harnessing solar energy, it employs hydrophilic nanoporous membranes for water movement and evaporation and an osmosis membrane for solute retention, aiming for efficient seawater desalination without external power and pressure. Energy and exergy analyses reveal key efficiency factors. Results reveal significant pressure gradients across the osmosis membrane as a challenge. Optimizing salinity and adjusting the area ratio of the nanoporous membrane to the osmosis membrane is vital for stability and performance. Moreover, potential increases in water production from 0.97 kg/m2/h to 3.53 kg/m2/h with a five-stage setup, though benefits diminish with additional stages due to heat/exergy losses. The exergy efficiency increases from 0.14 % for a five-stage device to 0.18 % for a ten-stage system. This study demonstrates the solar-powered passive multi-stage osmosis device’s dependence on solar flux and highlights the importance of optimizing the air gap thickness and insulation to boost efficiency.

Flexible operation of nuclear hybrid energy systems for load following and water desalination

Nuclear hybrid energy systems (NHES) have the potential to provide dependable and emission-free electricity to the grid while also increasing the flexibility and reliability of the electrical grid. Molten salt reactor (MSR) technology can provide consistent, carbon-free electricity while also increasing efficiency, security, and sustainability and reducing nuclear waste. This study investigates the integration of Molten Salt Reactors (MSR) and conventional Pressurized Water Reactors (PWR) with desalination technologies: Direct Contact Membrane Distillation (DCMD), Multi-Stage Flash Distillation (MSFD), and Reverse Osmosis (RO). Dynamic first-principles models were developed and tested using real grid data from the New York Independent System Operator. The results demonstrate that nuclear power is capable of flexibly responding to changing grid demand while simultaneously producing clean water, particularly during periods of low electricity demand. The MSR-RO system was found to be the most efficient in electricity generation and water production, and all hybrid systems reduced CO2 emissions by 356,000 to 682,000 tons annually. Economic analysis reveals that nuclear desalination technologies are cost-competitive with conventional systems, especially when paired with RO. These findings confirm the technical feasibility and environmental benefits of nuclear hybrid systems for sustainable electricity and water production.