Poverty, food insecurity and climate change are global issues facing humanity, threatening social, economic and environmental sustainability. Greenhouse cultivation provides a potential solution to these challenges. However, some greenhouses operate inefficiently and need to be optimized for more economical and cleaner crop production. In this paper, an economic model predictive control (EMPC) method for a greenhouse is proposed. The goal is to manage the energy-water‑carbon-food nexus for cleaner production and sustainable development. First, an optimization model that minimizes the greenhouse's operating costs, including costs associated with greenhouse heating/cooling, ventilation, irrigation, carbon dioxide (CO) supply and carbon emissions taking into account both the CO equivalent (CO-eq) emissions caused by electrical energy consumption and the negative emissions caused by crop photosynthesis, is developed and solved. Then, a sensitivity analysis is carried out to study the impact of electricity price, supplied CO price and social cost of carbon (SCC) on the optimization results. Finally, a model predictive control (MPC) controller is designed to track the optimal temperature, relative humidity, CO concentration and incoming radiation power in presence of system disturbances. Simulation results show that the proposed approach increases the operating costs by R186 (R denotes the South African currency, Rand) but reduces the total cost by R827 and the carbon emissions by 1.16 tons when compared with a baseline method that minimizes operating costs only. The total cost is more sensitive to changes in SCC than that in electricity price and supplied CO price. The MPC controller has good tracking performance under different levels of system disturbances. Greenhouse environmental factors are kept within specified ranges suitable for crop growth, which increases crop yields. This study can provide effective guidance for growers' decision-making to achieve sustainable development goals.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.scitotenv.2022.157756 | DOI Listing |
Med Sci Monit
January 2025
Department of Nephrology, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China.
BACKGROUND For patients with end-stage renal disease, arteriovenous fistulas (AVFs) are often used for hemodialysis, but stenosis can impair their function. Traditional inpatient procedures to address AVF stenosis are effective but resource-intensive, prompting the need for alternative approaches like day surgery to optimize care and reduce costs. This study evaluated the feasibility of a day surgery model for AVF stenosis treatment in maintenance hemodialysis (MHD) patients, aiming to develop a cost-effective and high-quality care model.
View Article and Find Full Text PDFNutrients
January 2025
Faculty of Food Science and Nutrition, University of Iceland, 102 Reykjavík, Iceland.
Background: Malnutrition predicts poor outcomes following hip fracture, affecting patient recovery, healthcare performance, and costs. Evidence-based guidelines recommend multicomponent, interdisciplinary nutrition care to improve intake, reduce complications, and enhance outcomes. This study examines global variation in oral nutrition support for older (65+ years) hip fracture inpatients.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Mechanical and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Silicon carbide (SiC) metal oxide semiconductor field-effect transistors (MOSFETs) are a future trend in traction inverters in electric vehicles (EVs), and their thermal safety is crucial. Temperature-sensitive electrical parameters' (TSEPs) indirect detection normally requires additional circuits, which can interfere with the system and increase costs, thereby limiting applications. Therefore, there is still a lack of cost-effective and sensorless thermal monitoring techniques.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. In real production environments, the amount of time series data that can be collected during the manufacturing process is limited, which can lead to a decline in model performance.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Free-Space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, China.
To achieve real-time deep learning wavefront sensing (DLWFS) of dynamic random wavefront distortions induced by atmospheric turbulence, this study proposes an enhanced wavefront sensing neural network (WFSNet) based on convolutional neural networks (CNN). We introduce a novel multi-objective neural architecture search (MNAS) method designed to attain Pareto optimality in terms of error and floating-point operations (FLOPs) for the WFSNet. Utilizing EfficientNet-B0 prototypes, we propose a WFSNet with enhanced neural architecture which significantly reduces computational costs by 80% while improving wavefront sensing accuracy by 22%.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!