In the wake of the era of big data, the techniques of deep learning have become an essential research direction in the machine learning field and are beginning to be applied in the steel industry. The sintering process is an extremely complex industrial scene. As the main process of the blast furnace ironmaking industry, it has great economic value and environmental protection significance for iron and steel enterprises. It is also one of the fields where deep learning is still in the exploration stage. In order to explore the application prospects of deep learning techniques in iron ore sintering, a comprehensive summary and conclusion of deep learning models for intelligent sintering were presented after reviewing the sintering process and deep learning models in a large number of research literatures. Firstly, the mechanisms and characteristics of parameters in sintering processes were introduced and analysed in detail, and then, the development of iron ore sintering simulation techniques was introduced. Secondly, deep learning techniques were introduced, including commonly used models of deep learning and their applications. Thirdly, the current status of applications of various types of deep learning models in sintering processes was elaborated in detail from the aspects of prediction, controlling, and optimisation of key parameters. Generally speaking, deep learning models that could be more effectively implemented in more situations of the sintering and even steel industry chain will promote the intelligent development of the metallurgical industry.

This work describes the design of an internet of things (IoT)-based prototype system for outdoor agriculture farmland that employs low-cost sensors for detecting basic agriculture parameters such as soil moisture level, air temperature, and air humidity to irrigate the farmland automatically and manually while tracing the water volume. Therefore, since the water requirement varies according to plant type and plant growth stages, this research focuses on a feature where user can adjust the soil moisture level from software according to the plant type and age stages so that a suitable moisture level can be maintained for plants. Apart from that, the system also has a feature to adjust the temperature threshold limit from the application software so that when the temperature sensor value exceeds the respective threshold limits, it sprinkles the water for a limited time so that plants are not damaged due to overheating. Google sheets is used for cloud storage, and Wi-Fi is used to send data to the cloud using the HTTP protocol. The Blynk application software is used to monitor and control purposes.

The most important control parameters in the methanol distillation process, which are directly related to product quality and yield, are the temperature, pressure and water content of the finished product at the top of the column. In order to adapt to the development trend of modern industrial technology to be more accurate, faster and more stable, the fusion of multi-sensor data puts forward higher requirements. Traditional control methods, such as PID control and fuzzy control, have the disadvantages of low heterogeneous data processing capability, poor response speed and low control accuracy when dealing with complex industrial process detection and control. For the control of tower top temperature and pressure in the methanol distillation industry, this study innovatively combines generative artificial intelligence and a type II fuzzy neural network, using a GAN for data preprocessing and a type II fuzzy neural network for steady-state inverse prediction to construct the GAN-T2FNN temperature and pressure control model for an atmospheric pressure tower. Comparison experiments with other neural network models and traditional PID control models show that the GAN-T2FNN model has a better performance in terms of prediction accuracy and fitting effect, with a minimum MAE value of 0.1828, which is more robust, and an R² Score of 0.9854, which is closer to 1, for the best overall model performance. Finally, the SHAP model was used to analyze the influence mechanism of various parameters on the temperature and pressure at the top of the atmospheric column, which provides a more comprehensive reference and guidance for the precise control of the methanol distillation process.

Energy conservation and optimization remains the top researched field for wireless sensor networks, which is one of a subsets and the underlying communication medium for Internet of Things (IoT) devices. These constrained IoT devices are mostly battery operated and therefore requires robust and optimized algorithms to improve resources utilization which inherently increases the life-span for these devices without compromising Quality of Service (QoS). The communication radios on these nodes are the most power hogging components. Therefore, a major focus has always been on MAC and cross-layer protocols to optimize the duty cycle of radios for the conservation of energy. This paper presents a unique scheme for dynamically adjusting the duty cycle of nodes based on the arrival of incoming infrequent source node sensor data over which eliminates the need for frequent periodic channel assessment for network activity. The proposed scheme also makes use of ultra-low wakeUp receivers on the receiver nodes to further aid the node in energy conservation. In this paper, we describe the details of our design scheme, implementation and evaluation details in Contiki OS and Cooja simulator. The results are micro-benchmarked with ContikiMAC and X-MAC protocols, and an improvement in radio duty cycle is reported for lighter network traffic.