Reduction of NOx Emission from the Cement Industry in South Korea: A Review

Multi-index control strategy from cement calcination denitration system: a model predictive control method for combined control of nitrogen oxide and ammonia escape

The cement industry is one of the main sources of NOx emissions, and automated denitration systems enable precise control of NOx emission concentration. With non-linearity, time delay and strong coupling data in cement production process, making it difficult to maintain stable control of the denitration system. However, excessive pursuit of denitration efficiency is often prone to large ammonia escape, causing environmental pollution. A multi-objective prediction model combining time series and a bi-directional long short-term memory network (MT-BiLSTM) is proposed to solve the data problem of the denitration system and achieve simultaneous prediction of NOx emission concentration and ammonia escape value. Based on this model, a model predictive control framework is proposed and a control strategy of denitration system with multi-index model predictive control (MI-MPC) is built based on neural networks. In addition, the differential evolution (DE) algorithm is used for rolling optimization to find the optimal solution and to obtain the best control variable parameters. The control method proposed has significant advantages over the traditional PID (proportional integral derivative) controller, with a 3.84% reduction in overshoot and a 3.04% reduction in regulation time. Experiments prove that the predictive control framework proposed in this paper has better stability and higher accuracy, with practical research significance.

A Review on Pollution Treatment in Cement Industrial Areas: From Prevention Techniques to Python-Based Monitoring and Controlling Models

Anthropogenic climate change, global warming, environmental pollution, and fossil fuel depletion have been identified as critical current scenarios and future challenges. Cement plants are one of the most impressive zones, emitting 15% of the worldwide contaminations into the environment among various industries. These contaminants adversely affect human well-being, flora, and fauna. Meanwhile, the use of cement-based substances in various fields, such as civil engineering, medical applications, etc., is inevitable due to the continuous increment of population and urbanization. To cope with this challenge, numerous filtering methods, recycling techniques, and modeling approaches have been introduced. Among the various statistical, mathematical, and computational modeling solutions, Python has received tremendous attention because of the benefit of smart libraries, heterogeneous data integration, and meta-models. The Python-based models are able to optimize the raw material contents and monitor the released pollutants in cement complex outputs with intelligent predictions. Correspondingly, this paper aims to summarize the performed studies to illuminate the resultant emissions from the cement complexes, their treatment methods, and the crucial role of Python modeling toward the high-efficient production of cement via a green and eco-friendly procedure. This comprehensive review sheds light on applying smart modeling techniques rather than experimental analysis for fundamental and applied research and developing future opportunities.

Recent trends in vanadium-based SCR catalysts for NOx reduction in industrial applications: stationary sources

Vanadium-based catalysts have been used for several decades in ammonia-based selective catalytic reduction (NH₃-SCR) processes for reducing NO_x emissions from various stationary sources (power plants, chemical plants, incinerators, steel mills, etc.) and mobile sources (large ships, automobiles, etc.). Vanadium-based catalysts containing various vanadium species have a high NO_x reduction efficiency at temperatures of 350-400 °C, even if the vanadium species are added in small amounts. However, the strengthening of NO_x emission regulations has necessitated the development of catalysts with higher NO_x reduction efficiencies. Furthermore, there are several different requirements for the catalysts depending on the target industry and application. In general, the composition of SCR catalyst is determined by the components of the fuel and flue gas for a particular application. It is necessary to optimize the catalyst with regard to the reaction temperature, thermal and chemical durability, shape, and other relevant factors. This review comprehensively analyzes the properties that are required for SCR catalysts in different industries and the development strategies of high-performance and low-temperature vanadium-based catalysts. To analyze the recent research trends, the catalysts employed in power plants, incinerators, as well as cement and steel industries, that emit the highest amount of nitrogen oxides, are presented in detail along with their limitations. The recent developments in catalyst composition, structure, dispersion, and side reaction suppression technology to develop a high-efficiency catalyst are also summarized. As the composition of the vanadium-based catalyst depends mostly on the usage in stationary sources, various promoters and supports that improve the catalyst activity and suppress side reactions, along with the studies on the oxidation state of vanadium, are presented. Furthermore, the research trends related to the nano-dispersion of catalytically active materials using various supports, and controlling the side reactions using the structure of shaped catalysts are summarized. The review concludes with a discussion of the development direction and future prospects for high-efficiency SCR catalysts in different industrial fields.