Real-time Temperature Control Research Articles

Optimizing Catfish Farming Through IoT-Driven Water Quality Management

General Background: Catfish play a significant role in the global fishing industry, necessitating effective farming practices to enhance productivity and sustainability. Specific Background: However, challenges such as maintaining optimal water quality in aquaculture ponds, particularly concerning ammonia levels and temperature fluctuations, pose threats to catfish health and production efficiency. Knowledge Gap: Previous research has often focused on single-parameter monitoring, lacking comprehensive systems that integrate real-time data for both ammonia and temperature management. Aims: This study aims to develop an advanced Internet of Things (IoT) system for real-time monitoring and control of ammonia levels and water temperature in catfish ponds, facilitating optimal farming conditions. Results: The implemented system utilizes two sensors to measure ammonia and temperature, integrated with the Blynk IoT platform for remote monitoring. The findings indicate that when water temperature exceeds 26 °C, the water pump activates to regulate temperature, while ammonia levels above 5 NH3 trigger the aerator to enhance oxygen levels and reduce ammonia toxicity. Novelty: This research innovatively combines multiple sensors and modern IoT applications, offering a more robust solution for aquaculture management compared to previous studies. Implications: The monitoring system effectively maintains water quality, enhancing catfish farming sustainability and efficiency, emphasizing the need for technology integration in aquaculture to effectively tackle environmental challenges. Highlights: Real-time Monitoring: Continuous tracking of ammonia and temperature levels. Automated Control: Activates pumps and aerators based on set thresholds. Enhanced Sustainability: Supports healthier catfish and optimizes production efficiency. Keywords: Catfish Farming, IoT, Water Quality Monitoring, Ammonia Control, Temperature Management

Real-time temperature control in rubber extrusion lines: a neural network approach

In rubber extrusion, precise temperature control is critical due to the process’s sensitivity to fluctuating parameters like compound behavior and batch-specific material variations. Rapid adjustments to temperature deviations are essential to ensure stable throughput and extrudate surface integrity. Based on our previous research, which initiated the development of a feedforward neural network (FNN) without real-world empirical application, we now present a real-time control system using artificial neural networks (ANNs) for dynamic temperature regulation. The underlying FNN was trained on a dataset comprising different ethylene propylene diene monomer (EPDM) rubber compounds, totaling 14,923 measurement points for each temperature value. After training, the FNN achieves remarkable precision, evidenced by a mean absolute percentage error (MAPE) of 0.68% and a mean squared error (MSE) of 0.63°C2 in predicting temperature variations. Its integration into the control system enables real-time responsiveness, allowing for adjustments to temperature deviations within an average timeframe of 68 ms. A key advantage over proportional-integral-derivative (PID) controllers is the ability to continuously learn and adjust to complex, non-linear, and batch-specific process dynamics. This adaptability results in enhanced process stability, as evidenced by inline manufacturing validation. Our paper presents the first ANN-based rubber extrusion control, demonstrating how machine learning techniques can be effectively leveraged for real-time, adaptive temperature control. Beyond rubber extrusion, this strategy has potential applications in various polymer processing and other industries requiring precise temperature control. Future trends may involve the integration of online learning techniques and the expansion of interconnected manufacturing processes.

Numerical analysis and process optimization of electron beam melting for superalloys

This study aims to determine the optimal parameters for the electron beam melting process by employing a modified transient three-dimensional thermal model. The validity of the thermal model is confirmed through experimental results. Moreover, the proposed method establishes mathematical relationships between the resulting molten pool temperature and process parameters. A broader range of process parameters for a new Ni–Co-based superalloy was achieved by investigating the effects of scanning velocities (10–100 mm/s), scanning radius (20–60 mm), and melting mass (1500–3100 g) on temperature at various electron beam power levels (10–30 kW). Regression models were established between scanning velocities-temperature, and scanning radius-temperature under different electron beam power levels. These models laid the foundation for real-time automatic control of molten pool temperature. Significantly, orthogonal experimental design and response surface method are employed to conduct a multi-objective optimization based on sixteen experiments and three parameters. The results of multi-objective optimization indicate that with a smelting mass of 1.5 kg, an electron beam power of 11 kW, a scanning radius of 60 mm, and a scanning velocity of 75 mm/s, the energy consumption is 34.95 kW h, and the theoretical mass loss of Ni element is 0.002 kg. The desirability is 91.2 %.

Real-Time Temperature-Controlled Retinal Laser Irradiation in Rabbits.

Subdamaging thermal retinal laser therapy has the potential to induce regenerative stimuli in retinal diseases, but validated dosimetry is missing. Real-time optoacoustic temperature determination and control could close this gap. This study investigates a first in vivo application. Two iterations of a control module that were optically coupled in between a continuous-wave commercial laser source and a commercial slit lamp were evaluated on chinchilla rabbits. The module allows extraction of the temperature rise in real time and can control the power of the therapy laser such that a predefined temperature rise at the retina is quickly achieved and held constant. Irradiations with aim temperatures from 45°C to 69°C were performed on a diameter of 200 µm and a heating time of 100 ms. We analyzed 424 temperature-guided irradiations in nine eyes of five rabbits. The mean difference between the measured and aim temperature was -0.04°C ± 0.98°C. The following ED50 values for visibility thresholds could be determined: 58.6°C for funduscopic visibility, 57.7°C for fluorescein angiography, and 57.0°C for OCT. In all measurements, the correlation of tissue effect was higher to the temperature than to the average heating laser power used. The system was able to reliably perform temperature-guided irradiations, which allowed for better tissue effect control than simple power control. This approach could enhance the accuracy, safety, and reproducibility of thermal stimulating laser therapy. This study is a bridge between preclinical ex vivo experiments and a pilot clinical study.

Innovative IoT-based intelligent smart cup coaster for enhanced beverage experience

The Innovative IoT-Based Intelligent Smart Cup Coaster presented in this research introduces a transformative approach to the traditional beverage coaster by seamlessly integrating cutting-edge Internet of Things (IoT) technology. This intelligent coaster is designed to enhance the overall beverage experience, addressing the limitations of conventional coasters. Equipped with temperature, weight, and proximity sensors, the smart cup coaster communicates wirelessly with a dedicated mobile application, allowing users to personalize and control various aspects of their beverage enjoyment. Features such as real-time temperature control, weight sensing, proximity awareness, customizable LED lighting, and integration with virtual assistants redefine the user experience. By leveraging IoT capabilities, this smart coaster not only elevates the functionality of a commonplace object but also offers valuable insights into consumption patterns, encouraging hydration and providing a dynamic and interactive beverage experience for modern consumers.

Treatment of Spontaneous Tumors With Algorithmically Controlled Electroporation.

To study the safety and efficacy of algorithmically controlled electroporation (ACE) against spontaneous equine melanoma. A custom temperature sensing coaxial electrode was paired with a high voltage pulse generation system with integrated temperature feedback controls. Computational modeling and ex vivo studies were conducted to evaluate the system's ability to achieve and maintain target temperatures. Twenty-five equine melanoma tumors were treated with a 2000 V protocol consisting of a 2-5-2 waveform, 45 °C temperature set point, and integrated energized times of 0.005 s, 0.01 s, or 0.02 s (2500x, 5000x, and 10000x 2 μs pulses, respectively). Patients returned 20-50 days post treatment to determine the efficacy of the treatment. ACE temperature control algorithms successfully achieved and maintained target temperatures in a diverse population of spontaneous tumors with significant variation in tissue impedance. All treatments were completed successfully without and without adverse events. Complete response rates greater than 93% were achieved in all treatment groups. ACE is a safe and effective treatment for spontaneous equine melanoma. The temperature control algorithm enabled rapid delivery of electroporation treatments without prior knowledge of tissue electrical or thermal properties and could adjust to real time changes in tissue properties. Real time temperature control in electroporation procedures enables treatments near critical structures where thermal damage is contraindicated. Unlike standard approaches, ACE protocols do not require extensive pretreatment planning or knowledge of tissue properties to determine an optimal energy delivery rate and they can account for changes in tissue state (e.g., perfusion) in real time to simultaneously minimize treatment time and potential for thermal damage.

A systematic review of the design considerations for the operation and maintenance of small-scale biogas digesters

This review investigates small-scale biogas digesters' design and construction considerations to address biogas digesters' failures shortly after installation. The frequent failures of small-scale or household biogas digesters negatively affect its adoption as a clean domestic cooking fuel in developing countries, affecting the achievement of Sustainable Development Goal (SDG) 7. The study considered Scopus database-indexed peer-reviewed journals published between 2000 and 2022. Selected papers focussed on real-time monitoring, stirring mechanisms, and temperature control systems based on predefined inclusion and exclusion criteria with initial search results of 4751 documents, narrowing to 55 papers. The PRISMA 2020 statement was adopted to conduct the study. The study highlights the importance of incorporating a real-time monitoring system as a design factor in small-scale biogas digesters for successful operation and maintenance. The study's findings may be helpful to practitioners, policymakers, and researchers promoting sustainable energy and waste management solutions in low-resource settings.

FPGA real-time melt-pool temperature control in Directed Energy Deposition

Sensing and control are key in laser-based Directed Energy Deposition (DED-LB) in terms of repeatability and good quality builds. This work presents a real-time control system where the melt-pool temperature is monitored, and the laser power is adjusted in closed-loop as control parameter. The control system includes a Field-Programmable Gate Array (FPGA) for the real-time estimation of the melt-pool temperature extracted from data from a coaxial high-speed hyperspectral camera, and a Proportional-Integral (PI) controller for the laser power regulation. The controller gains are tuned to ensure constant temperature during deposition. However, different sets of gains, with differences in the response time and transient behavior of the controller, achieve this goal but produce different deposition quality. The presented paper discusses the impact of the different controller gains on the melt-pool temperature and melt-pool size during the deposition of thin-walls and squared spirals, and compares open-loop (without control) and closed-loop (with control) cases.

Wellbore-heat-transfer-model-based optimization and control for cooling downhole drilling fluid

To address the two critical issues of evaluating the necessity of implementing cooling techniques and achieving real-time temperature control of drilling fluids underground in the current drilling fluid cooling technology, we first established a temperature and pressure coupled downhole heat transfer model, which can be used in both water-based and oil-based drilling fluid. Then, fourteen factors, which could affect wellbore temperature, were analyzed. Based on the standard deviation of the downhole temperature corresponding to each influencing factor, the influence of each factor was quantified. The influencing factors that can be used to guide the drilling fluid's cooling technology were drilling fluid thermal conductivity, drilling fluid heat capacity, drilling fluid density, drill strings rotation speed, pump rate, viscosity, ROP, and injection temperature. The nondominated sorting genetic algorithm was used to optimize these six parameters, but the optimization process took 182 min. Combining these eight parameters' influence rules with the nondominated sorting genetic algorithm can reduce the optimization time to 108 s. Theoretically, the downhole temperature has been demonstrated to increase with the inlet temperature increasing linearly under quasi-steady states. Combining this law and PID, the downhole temperature can be controlled, which can reduce the energy for cooling the surface drilling fluid and can ensure the downhole temperature reaches the set value as soon as possible.

Mathematical Model of the Layer-by-Layer FFF/FGF Polymer Extrusion Process for Use in the Algorithm of Numerical Implementation of Real-Time Thermal Cycle Control.

An approach for improving and maintaining a consistent weld quality of the deposited material during the FFF printing process is proposed. The approach is based on the analysis of the printing process thermal cycle and the real-time nozzle temperature control. The mathematical model of the FFF printing process has been developed with the use of real-time control in the algorithm of numerical implementation. The successful solution of the thermal conductivity problem made it possible to determine segment-wise heating settings for use during the printing process, resulting in a high and stable quality of welding. Comparison of the results of modeling with other well-known mathematical models of the FFF printing process and experimental results showed the adequacy of the proposed model. A maximum deviation of 17.7% between the simulation results and the thermography data was observed. The proposed model was verified using rectangular 3D polylactide shapes printed with and without regulation of the power of the heat source according to the previously estimated settings. The overall quality of regulation, stability of the system, and the PI coefficients of the controller were evaluated using a simulated model of the control system. The results of the experiment fully correspond with the modeling results.

Energy and battery management systems for electrical vehicles: A comprehensive review & recommendations

Electric vehicle technology has recently drawn a lot of interest on a global scale due to improved performance in its efficiency and the capability to solve the problems of carbon emission. As such, electric vehicles are the key to achieving sustainable development goals. This review article analyzes deeply the previous technical developments of electric vehicles, focusing on important topics like battery management systems, technologies of power electronics, techniques of charging, and the relevant algorithms and improvements. In addition, several critical problems, and difficulties are presented in order to pinpoint the gaps in the literature. To address the analysis of battery behavior, battery condition monitoring, real-time control design, temperature control, fault diagnostics, and efficiency of battery model are considered. This study highlighted the estimation techniques that predict the internal battery conditions such as internal temperature, state of health, and state of charge, which are difficult to be directly monitored and determined. A lithium-ion battery, a super-capacitor, and related bidirectional DC/DC converters constitutes the infrastructure of a hybrid power system. This review offers useful and practical recommendations for the future development of electric vehicle technology which in turn help electric vehicle engineers to be acquainted with effective techniques of battery storage, battery charging strategies, converters, controllers, and optimization methods to satisfy the requirements of sustainable development goals. Accordingly, this review article will be a platform and future guide for those who are interesting in the field of energy management and its development.

Transformasi Pertanian Dalam Ruangan: Hidroponik Cerdas Berbasis IoT

The growth of technology has had a significant impact on the agricultural sector, including the method of hydroponic cultivation. According to the Basic Health Research (Riskesdas) conducted by the Ministry of Health, 90% of the Indonesian population lacks sufficient vegetable consumption. The World Health Organization (WHO) recommends a daily vegetable intake of around 400 grams for adults, which can be fulfilled through indoor hydroponic gardens. However, hydroponic cultivation requires intensive monitoring and control, posing a challenge for busy urban communities. Therefore, one solution to address the challenges in hydroponic farming is leveraging Internet of Things (IoT) technology and utilizing Real-time Clock (RTC) to create a Smart Indoor Hydroponic Garden system that enables real-time control of water pH and room temperature using temperature sensors and water quality sensors. This system can be utilized by urban individuals who lack access to open land, allowing them to meet their daily vegetable needs directly from home.

Innovative liquid metal strategy for real-time thermal control in additive manufacturing

Metal additive manufacturing (AM) involves rapid cycles of melting and solidification, often resulting in significant thermal stress variations, microcrack formation, and compromised structural integrity, especially for complex structures. Effective strategies are crucial for regulating temperature and stress distribution during laser-based metal AM processes; however, traditional thermal management approaches suffer from limitations in action area and response speed. In this study, the novel Liquid Metal-Assisted Additive Manufacturing (LMAAM) process is developed to enhance real-time temperature and stress control during deposition. This is the first known attempt to utilize liquid tin as an auxiliary thermal management material for AM. Numerical simulations and experimental analysis are performed to determine the relationship between LMAAM process conditions and resultant material microstructural properties. The results show that compared to AM without active thermal management, the incorporation of liquid tin yields an approximate 20 % increase in the cooling rate, a roughly 30 % reduction in residual tensile stress, a decrease in part grain size, and a roughly 30 % enhancement in microhardness. LMAAM technology opens up new possibilities for metal AM of highly reliable and intricate structures like multi-chambers, multi-channels, and deep blind holes.

Integrated photothermal agents with fluorescence intensity ratio/lifetime dual-mode temperature sensing

Non-invasive photo-thermal therapy (PTT) has been proposed as a powerful method for cancer treatment, in which accurate temperature monitoring is necessary to prevent ineffective therapy or damage to normal cells during the process of PTT. This promotes the design and development of photothermal agents (PTAs) with real-time temperature control and excellent photothermal conversion. Here, the designed dual-function photothermal agents YVO4: Nd3+, Yb3+@IR-780 were obtained, while the temperature detection behavior based on the fluorescence intensity ratio (FIR) of Nd3+ (4F3/2 → 4I11/2) to Yb3+ (2F5/2 → 2F7/2) and the temperature-dependent Yb3+ lifetime were investigated in detail. The advantages of fluorescence lifetime thermometry in the biological field were confirmed by the internal temperature detection of pork tissue through comparison with fluorescence intensity ratio technology. The photo-thermal conversion efficiency of samples YVO4: Nd3+, Yb3+@IR-780 reached about 34.3% and its potential application was also evaluated by the sterilization of E. coli and S. aureus, which shows that IR-780 coated nano-materials have excellent photo-thermal conversion performance. Results illuminated that the present photothermal agents with temperature self-monitoring had a bright future in the process of photothermal therapy.

Temperature Control Performance of a Built-In Motor Spindle by Developed Temperature Feedback Control System

The temperature control performance of a developed temperature feedback control system was experimentally investigated. The control system was based on a real-time temperature control of a cooling fluid. In particular, this study focused on the temperature control performance of a built-in motor spindle that used the developed temperature fedback control system. The built-in motor used in the study had water cooling jackets. The temperature of the built-in motor spindle was measured and feedback into the developed temperature feedback control system. Temperature control accuracy of the built-in motor spindle under steady state was then assessed. Furthermore, the effects of the time-variant changes in spindle rotation and ambient temperature on the performance of the temperature control system was investigated. The results of the experiments show that the temperature control accuracy of the built-in motor spindle under steady state condition was ±0.03°C. The temperature control performance of the built-in motor spindle under changes in the rotational speed of the spindle was examined. The experimental results show that the temperature change of the spindle could be suppressed to a maximum of approximately 0.3°C under transient state during sudden change in spindle speed. In addition, the effects of the changes in ambient and cooling water temperatures, which simulated actual environmental operating conditions, on the spindle temperature were investigated. The results show that the change in the spindle temperature could be suppressed by approximately less than ±0.1°C. These experimental results indicate that the developed temperature feedback control system achieved high temperature control accuracy and high response for the built-in motor spindle. In particular, the developed control system successfully controlled the time-variant change in the generated heat, thereby improving the thermal stability of the machine tool spindle.

A state-space model-based temperature control system for laser remanufacturing molten pool

ABSTRACT Laser remanufacturing is a key technology for remanufacturing, which has been widely used in aerospace, metallurgy, energy and other fields. However, the problem of shape control has been the bottleneck that restricts the scale and industrial application. In the laser remanufacturing process, the molten pool temperature is the critical to determine the shape and performance stability of remanufactured parts. Based on this, the paper proposes a real-time online control method for laser remanufacturing molten pool temperature based on state space model, which solves the problem of difficult to establish control mechanism model and multiple variable constraints. Develop a real-time online control system for molten pool temperature of laser remanufacturing process based on state space model and incremental integral separation PID control, which uses a single sampling point as temperature feedback, solving the problem of lag generated by traditional feedback cycle, realizing real-time online control of molten pool temperature of laser remanufacturing process. Finally, the experimental results show that the developed molten pool temperature control system can effectively eliminate the sudden and gradual temperature change in the deposition process and significantly improve the thermal accumulation phenomenon in the deposition process, thus improving the forming quality and performance stability of remanufactured parts.

Integrated adversarial long short-term memory deep networks for reheater tube temperature forecasting of ultra-supercritical turbo-generators

As an important equipment of the ultra-supercritical turbine systems, real-time and accurate control of reheater tube temperature is the key to the normal operation of the system. To improve the accuracy of reheater tube temperature prediction, an integrated adversarial long short-term memory deep network is proposed in this study. Considering the correlation and dynamics between reheater temperature features, different types of single long short-term memory deep networks are batch mixed and stacked in parallel to integrate adversarial training. The training process shares network layer weight parameters to synthesize the decision boundary of each network and complete the dichotomous integration. The proposed networks predict information for the next ten time steps, wirelessly approximate the mapping relationship between multi-source input and output data, and maximize the prediction accuracy and generalization. The proposed networks with single long short-term memory deep networks and 21 algorithms are compared for temperature prediction on the reheater tubes of thermal power plants in Guangdong Province, China, from March 2020 to May 2020. Compared with 90 networks without an integrated adversary, the mean square error obtained by the integrated networks have a 90.85% reduction. Furthermore, compared with the algorithm with the second smallest error among the 22 algorithms, the mean absolute percentage error obtained by the integrated networks is significantly reduced by 99.66%, and the mean absolute error is reduced by 98.36%.

Multi-Wavelength Photoacoustic Temperature Feedback Based Photothermal Therapy Method and System.

Photothermal therapy (PTT) is a new type of tumor treatment technology that is noninvasive, repeatable, and does not involve radiation. Owing to the lack of real-time and accurate noninvasive temperature measurement technology in current PTT surgical procedures, empirical and open-loop treatment laser power control mode inevitably leads to overtreatment. Thermal radiation causes irreversible damage to normal tissue around cancer tissue and seriously affects the therapeutic effect of PTT and other therapies conducted at the same time. Therefore, real-time measurement and control of the temperature and thermal damage of the therapeutic target are critical to the success of PTT. To improve the accuracy and safety of PTT, we propose a multi-wavelength photoacoustic (PA) temperature feedback based PTT method and system. PA thermometry information at different wavelengths is mutually corrected, and the therapeutic light dose is regulated in real time to accurately control the treatment temperature. The experimental results on the swine blood sample confirm that the proposed method can realize real-time temperature measurement and control of the target area with an accuracy of 0.56 °C and 0.68 °C, demonstrating its good prospects for application.

An integrated dual-layer microfluidic platform for multiple respiratory viruses screening

Currently, the coronavirus disease 2019 (COVID-19) caused by the outbreak of a novel coronavirus (SARS-CoV-2) is spreading rapidly worldwide. Due to the high incidence of influenza coinciding with SARS-CoV-2, rapid detection is crucial to prevent spreading. Here, we present an integrated dual-layer microfluidic platform for specific and highly sensitive SARS-CoV-2, influenza viruses A (FluA) H1N1, H3N2, and influenza virus B (FluB) simultaneous detection. The platform includes a dual microchip (Dμchip) and a portable detection device for real-time fluorescence detection, temperature control and online communication. The Reverse Transcription Loop-mediated Isothermal Amplification (RT-LAMP) and Cas12a cleavage were performed on the Dμchip. The limit of detection (LoD) of the Dμchip assay was 10 copies for SARS-CoV-2, FluA H1N1, H3N2, and FluB RNAs. The Dμchip assay yielded no cross-reactivity against other coronaviruses, so it was suitable for the screening of multiple viruses. Moreover, the positive percentage agreement (PPA) and negative percentage agreement (NPA) of the assay were 97.9% and 100%, respectively, in 75 clinical samples compared to data from RT-PCR-based assays. Furthermore, the assay allowed the detection SARS-CoV-2 and influenza viruses in spiked samples. Overall, the present platform would provide a rapid method for the screening of multiple viruses in hospital emergency, community and primary care settings and facilitate the remote diagnosis and outbreak control of the COVID-19.

A novel hybrid intelligent model for molten iron temperature forecasting based on machine learning

<abstract> <p>To address the challenges of low accuracy and poor robustness of traditional single prediction models for blast furnace molten iron temperature, a hybrid model that integrates the improved complete ensemble empirical mode decomposition with adaptive noise, kernel principal component analysis, support vector regression and radial basis functional neural network is proposed for precise and stable iron temperature prediction. First, the complete ensemble empirical mode decomposition is employed to decompose the time series of iron temperature, yielding several intrinsic mode functions. Second, kernel principal component analysis is used to reduce the dimensionality of the multi-dimensional key variables from the steel production process, extracting the major features of these variables. Then, in conjunction with the K-means algorithm, support vector regression is utilized to predict the first column of the decomposed sequence, which contains the most informative content, evaluated using the Pearson correlation coefficient method and permutation entropy calculation. Finally, radial basis function neural network is applied to predict the remaining time series of iron temperature, resulting in the cumulative prediction. Results demonstrate that compared to traditional single models, the mean absolute percentage error is reduced by 54.55%, and the root mean square error is improved by 49.40%. This novel model provides a better understanding of the dynamic temperature variations in iron, and achieves a hit rate of 94.12% within a range of ±5℃. Consequently, this work offers theoretical support for real-time control of blast furnace molten iron temperature and holds practical significance for ensuring the stability of blast furnace smelting and implementing intelligent metallurgical processes.</p> </abstract>