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Environmental management for collections : alternative conservation strategies for hot and humid climates
\"A practical guide to the conservation of museum and other cultural heritage collections in hot and humid climates\"--Provided by publisher.
Book
WAAM system with interpass temperature control and forced cooling for near-net-shape printing of small metal components
In an attempt to find a solution similar to the FDM 3D printers which would allow cost-effective and reliable additive manufacturing of metal components, this paper proposes a three-axis WAAM system capable of reliably printing small, near-net-shape metal objects. The system consists of gas metal arc (GMA) process equipment, a three-axis CNC positioning system, the interpass temperature control and forced cooling of the base plate and the deposit. The main challenge addressed is the minimisation of shape distortions caused by excessive heat accumulation when printing small objects. The interpass temperature control uses an IR pyrometer to remotely measure the last deposited layer and a control system to keep the interpass temperature below the predefined value by stopping the deposition after each layer in order to allow the deposit to cool. This results in a stable and more repeatable shape of the deposit, even when the heat transfer conditions are changing during the build-up process. The combination of adaptive interlayer dwell time and forced cooling significantly improves system productivity. Open-source NC control and path generation software is used, which enables fast and easy creation of the control code. Different control methods are evaluated through the printing of simple walls, and the printing accuracy is evaluated by printing small shell objects. As the results show, the interpass temperature control allows small objects to be printed at near-net shape with a deviation of 2%, which means that successful printing of 3D shapes can be achieved without trial and error approach.
Journal Article
Internal Gas-Assisted Mold Temperature Control for Improving the Filling Ability of Polyamide 6 + 30% Glass Fiber in the Micro-Injection Molding Process
In micro-injection molding, the plastic filling in the cavity is limited by the frozen layer due to the rapid cooling of the hot melt when it comes into contact with the surface of the cavity at a lower temperature. This problem is more serious with composite materials, which have a higher viscosity than pure materials. Moreover, this issue is also more serious with composite materials that have a higher weight percentage of glass filer. In this article, a pre-heating step with the internal gas heating method was used to heat the cavity surface to a high temperature before the filling step to reduce the frozen layer and to improve the filling ability of the composite material (polyamide 6 + 30% glass fiber) in the micro-injection molding process. To heat the cavity surface, an internal gas-assisted mold temperature control (In-GMTC) system was used with a pulsed cooling system. We assessed different mold insert thicknesses (t) and gaps between the gas gate and the heating surface (G) to achieve rapid mold surface temperature control. The heating process was observed using an infrared camera, and the temperature distribution and the heating rate were analyzed. Thereafter, along with the local temperature control, the In-GMTC was used for the micro-injection molding cycle. The results show that, with a gas temperature of 300 °C and a gas gap of 3.5 mm, the heating rate reached 8.6 °C/s. The In-GMTC was also applied to the micro-injection molding process with a part thickness of 0.2 mm. It was shown that the melt flow length had to reach 24 mm to fill the cavity completely. The results show that the filling ability of the composite material increased from 65.4% to 100% with local heating at the melt inlet area when the gas temperature rose from 200 to 400 °C with a 20 s heating cycle.
Journal Article
Alleviation of Field Low-Temperature Stress in Winter Wheat by Exogenous Application of Salicylic Acid
Low temperature in later spring severely limits plant growth and causes considerable yield loss in wheat. In this study, the impacts of exogenous salicylic acid (SA) on plant growth, grain yield and key physiological parameters of wheat plants were investigated under field low-temperature conditions using a field air temperature control system (FATC). The results showed that low-temperature stress significantly decreased leaf net photosynthetic rate, plant height and biomass production of wheat plants at the jointing stage, resulting in a reduction in grain yield. Moreover, the growth period of wheat plants was prolonged by low-temperature stress. However, SA-treated plants significantly improved the photochemical efficiency of photosystem II, accumulation of osmo-protectants, activities of enzymatic antioxidants, and pool of non-enzymatic low molecular substances compared with non-SA-treated plants under low-temperature stress. Pretreatment with SA effectively alleviated low-temperature-induced reduction in leaf net photosynthetic rate, plant height, biomass production and grain yield as well as prolonging of growth period of wheat plants. However, SA-treated plants had no significant effects on the expression levels of cold-responsive genes compared with non-SA-treated plants under low-temperature stress. Our results demonstrated that exogenous application of SA is an appropriate strategy for wheat to resist late spring low-temperature stress under field conditions.
Journal Article
Active temperature control method based on time grating principle for the feed system of precision machine tool and its application
Variations in running conditions cause fluctuation in the temperature field of precision machine tools, which inevitably results in thermal errors. To meet the demands of dynamic and time-varying temperature control capability, an active temperature control (ATC) method based on time grating principle is proposed, and the ATC system is developed. The ATC system contains main-loop and sub-loops. The oil target temperature in the sub-loop is determined according to the running parameters and the matching principle of the generalized heat generation–dissipation power. In accordance with the time grating principle, dynamic and differential oil temperature control of each sub-loop is achieved via the inlet time regulation of high-temperature (H-t) or low-temperature (L-t) oil in the main-loop. The main-loop H-t and L-t oil target temperatures are determined by the target range of the sub-loop temperature. The dynamic distribution of the refrigeration capacity and proportional heating mode is adopted to control the temperatures of H-t and L-t oil. By focusing on the feed system of precision machine tool, we carry out both dynamic simulation study and verification experiments, and the results show that the ATC method and system can effectively regulate the temperature field of precision machine tools, thus improving the thermal accuracy of the precision machine tool.
Journal Article
Impact of Skin Temperature Control Variable on the Assimilation of Microwave Temperature-sounding Channels in Regional Numerical Weather Prediction
Accurate skin temperature is one of the critical factors in successfully assimilating satellite radiance data over land. However, model-simulated skin temperature may not be accurate enough. To address this issue, an extended skin temperature control variable (TSCV) approach is proposed in a variational assimilation framework, which also considers the background error correlation between skin temperature and atmospheric variables. A series of single observation tests and a 10-day cycling assimilation experiment were conducted to evaluate the impact of the TSCV approach on the assimilation of AMSU-A and ATMS (Advanced Technology Microwave Sounder) microwave temperature-sounding channels over land. The results of the single observation tests show that by applying the TSCV approach, not only the direct analysis of skin temperature is realized, but also the interaction between skin temperature and atmospheric variables can be achieved during the assimilation process. The results of the cycling experiment demonstrate that the TSCV approach improves the skin temperature analysis, which in turn reduces the RMSE of the surface variables and low-level air temperature forecasts. The TSCV approach also reduces the difference between the observed and simulated brightness temperatures of both microwave and infrared window channels over land, suggesting that the approach can facilitate the radiance simulation of these channels, thus contributing to the assimilation of window channels.
Journal Article
A new intelligent control strategy for CSTH temperature regulation based on the starfish optimization algorithm
Temperature regulation in nonlinear and highly dynamic processes such as the continuous stirred-tank heater (CSTH) is a challenging task due to the inherent system nonlinearities and disturbances. This study proposes a novel metaheuristic-driven control strategy, combining the two degrees of freedom-PID acceleration (2DOF-PIDA) controller with the recently developed starfish optimization algorithm (SFOA) for temperature control of the CSTH process. The 2DOF-PIDA controller enhances system performance by decoupling setpoint tracking and disturbance rejection, while the SFOA ensures optimal tuning of controller parameters by leveraging its powerful exploration and exploitation capabilities. Simulation results validate the effectiveness of the proposed approach, demonstrating improved tracking accuracy, disturbance rejection, and robustness compared to conventional methods. The combination of 2DOF-PIDA and SFOA provides a flexible and efficient solution for controlling highly nonlinear systems, with significant implications for industrial temperature regulation applications.
Journal Article
Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain
The management of a food supply chain is difficult and complex because of the product's short shelf-life, time-sensitivity, and perishable nature which must be carefully considered to minimize food waste. Temperature-controlled perishable food supply chain provides the highly crucial facilities necessary to maintain the quality and safety of the product. The storage temperature is the most vital factor in maintaining both the quality and shelf-life of a perishable food. Adequate storage temperature control ensures that perishable foods are transported to the end-users in good quality and safe to consume. This paper presents perishable food storage temperature control through mathematical optimal control model where the storage temperature is regarded as the control variable and the deterioration of the perishable food’s quality follows the first-order reaction. The optimal storage temperature for a single perishable food is determined by applying the Pontryagin's maximum principle to solve the optimal control model problem. For multi-temperature commodities supply chain, an unsupervised machine learning (ML) method, called
k
-means clustering technique is used to determine the temperature clusters for a range of perishables. Based on descriptive analysis, it is observed that the
k
-means clustering technique is effective in identifying the best suitable storage temperature clusters for quality control of multi-commodity supply chain.
Journal Article
AI powered blockchain framework for predictive temperature control in smart homes using wireless sensor networks and time shifted analysis
In the context of smart homes, efficiently managing temperature control while optimizing energy consumption and ensuring data security remains a significant challenge. Traditional thermostat-based systems lack predictive capabilities, and energy consumption often spikes during peak hours, leading to inefficiency. Additionally, the security of sensitive data in smart home environments is a growing concern. This paper presents a novel AI-powered blockchain framework for predictive temperature control in smart homes, leveraging wireless sensor networks (WSNs) and time-shifted analysis. The framework integrates machine learning (ML) algorithms for predictive temperature management, blockchain technology for secure data handling, and edge computing for real-time data processing, resulting in a highly efficient and secure system. Key innovations include the dynamic detection of heating and cooling events, predictive scheduling based on historical data, and blockchain-based decentralized energy trading. Performance evaluation demonstrates that the system accurately detects radiator heat-on events with a 28.5% success rate, while radiator cooling event detection achieves 37.3% accuracy. Scheduled heat-on events were triggered with 68.4% reliability, and the system’s machine learning component successfully reduced energy consumption by 15.8% compared to traditional thermostat controls, by adjusting heating based on predictive analysis. Additionally, the time-shifted data processing reduces peak-time computational load by 22%, contributing to overall energy efficiency and system scalability. The integration of blockchain ensures tamper-proof data security, eliminating unauthorized data access, and improving trust in smart home environments. These results illustrate the potential of combining AI, blockchain, and WSNs to create a robust, energy-efficient, and secure smart home temperature control system, offering significant improvements over traditional solutions.
Journal Article
Brine recovery from reverse osmosis effluents using an automatic temperature control system: salt crystallization
The improper brine effluent disposal from the reverse osmosis (RO) process of the industry and mining sites poses various environmental problems including impaired soil ability to produce crops, difficulties for breeding and migrating birds. Various treatment methods reported to provide a short-term solution in which separation of the mixed salt byproducts remains a great challenge. The present study aimed at assessing the performance of locally available automatic temperature control (ATC) system to recover brine from RO effluents through salt crystallization. Laboratory trials via batch experiment containing individual and mixed samples of NaCl·2H2O, Na2SO4·10H2O, and Na2CO3·10H2O were conducted to evaluate the influence of freezing temperature, concentration, and contact time. The maximum recovery efficiencies of 85.3% (NaCl·2H2O), 93.3% (Na2SO4·10H2O), and 32.0% (Na2CO3·10H2O) of the individual samples were achieved at 72 h (−26 °C), 96 h (–10 °C), and 2 h (–2 °C), respectively. For mixed samples at −10 °C and 3 h in 50 mL, 29.37 g (65.27%) were recovered with ionic constituents such as Na+ (34.1%), Cl− (1.6%), SO42− (11.3%), CO32− (32.4%), and impurities (20.57%). The findings of this study suggested that ATC could be used as an alternative technology for brine recovery from RO process of industry and mining operation sites.
Journal Article