Modeling and Optimization of the Cement Calcination Process for Reducing NOx Emission Using an Improved Just-In-Time Gaussian Mixture Regression

PI gain tuning for pressure-based MFCs with Gaussian mixture model

A vast number of mass flow controllers (MFCs) are used in semiconductor industry. For the stable supply, an efficient production method of MFC is required. The gain tuning of the proportional-integral (PI) control to realize a setting flow rate is essential for efficient mass production. The gains are tuned to meet the specifications required for evaluation indices of response time and overshoot amount in a step response waveform. The tuning is complicated especially for the case of pressure-based MFCs. In this paper, we propose a simple method for the PI gain tuning using the Gaussian mixture model (GMM) and the direct inverse analysis applicable to the pressure-based MFCs' production. The relationship between the gains and evaluation indices for a standard unit of the MFC is modeled as the GMM. The direct inverse analysis calculates the difference between the standard and a test unit. Under the assumption that the difference can be compensated by a simple shift, gains likely to meet the specifications for the test unit are searched. We applied the method to seven test units. The result showed that the gains of all the test units were tuned within only a few iterations whose numbers were much less than the conventional manual tuning method, and there was no untunable unit.

Soft sensing of NOx emission from waste incineration process based on data de-noising and bidirectional long short-term memory neural networks

Continuous emission monitoring system is commonly employed to monitor NOx emissions in municipal solid waste incineration (MSWI) processes. However, it still encounters the challenges of regular maintenance and measurement lag. These issues significantly impact the accurate and stable control of NOx emissions. Therefore, developing a soft NOx emission sensor to complement hardware monitoring becomes imperative. Considering data noise, dynamic nonlinearity, time series characteristics and volatility in the MSWI process, this article introduces a soft sensor model for NOx emission prediction utilizing the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN)-wavelet threshold (WT) method and bidirectional long short-term memory (Bi-LSTM). Firstly, the original data signal is decomposed into a group of intrinsic mode functions (IMFs) using the CEEMDAN. Subsequently, the WT processes the high-frequency IMFs that are noise-dominant. Then, all IMFs are reconstructed to obtain the denoized signal. Finally, the Bi-LSTM model is employed to predict NOx emissions. Compared to conventional modelling approaches, the model proposed in this article demonstrates the best predictive performance. The mean absolute percentage error, root-mean-squared error and average absolute error on the test set of the proposed model are 3.75%, 5.34 mg m-3 and 4.34 mg m-3, respectively. The proposed model provides a new method to soft sensing NOx emissions. It holds significant practical value for precise and stable monitoring of NOx emissions in MSWI processes and provides a reference for research on modelling key process parameters.

Utilizing Undissolved Portion (UNP) of Cement Kiln Dust as a Versatile Multicomponent Catalyst for Bioethylene Production from Bioethanol: An Innovative Approach to Address the Energy Crisis

This study focuses on upcycling cement kiln dust (CKD) as an industrial waste by utilizing the undissolved portion (UNP) as a multicomponent catalyst for bioethylene production from bioethanol, offering an environmentally sustainable solution. To maximize UNP utilization, CKD was dissolved in nitric acid, followed by calcination at 500 °C for 3 h in an oxygen atmosphere. Various characterization techniques confirmed that UNP comprises five different compounds with nanocrystalline particles exhibiting an average crystal size of 47.53 nm. The UNP catalyst exhibited a promising bioethylene yield (77.1%) and selectivity (92%) at 400 °C, showcasing its effectiveness in converting bioethanol to bioethylene with outstanding properties. This exceptional performance can be attributed to its distinctive structural characteristics, including a high surface area and multiple-strength acidic sites that facilitate the reaction mechanism. Moreover, the UNP catalyst displayed remarkable stability and durability, positioning it as a strong candidate for industrial applications in bioethylene production. This research underscores the importance of waste reduction in the cement industry and offers a sustainable path toward a greener future.

Evaluation of adaptive neuro-fuzzy inference system-genetic algorithm in the prediction and optimization of NOx emission in cement precalcining kiln

The increasing demand for cement due to urbanization growth in Africa countries may result in an upsurge of pollutants associated with its production. One major air pollutant in cement production is nitrogen oxides (NOx) and reported to cause serious damage to human health and the ecosystem. The operation of a cement rotary kiln NOx emission was studied with plant data using the ASPEN Plus software. It is essential to understand the effects of calciner temperature, tertiary air pressure, fuel gas, raw feed material, and fan damper on NOx emissions from a precalcining kiln. In addition, the performance capability of adaptive neuro-fuzzy inference systems and genetic algorithms (ANFIS-GA) to predict and optimize NOx emissions from a precalcining cement kiln is evaluated. The simulation results were in good agreement with the experimental results, with root mean square error of 2.05, variance account (VAF) of 96.0%, average absolute deviation (AAE) of 0.4097, and correlation coefficient of 0.963. Further, the optimal NOx emission was 273.0 mg/m³, with the parameters as determined by the algorithm were calciner temperature at 845 °C, tertiary air pressure - 4.50 mbar, fuel gas of 8550 m³/h, raw feed material 200 t/h, and damper opening of 60%. Consequently, it is recommended that ANFIS should be combined with GA for effective prediction, and optimization of NOx emission in cement plants.

Advanced Process Control for Clinker Rotary Kiln and Grate Cooler

The cement industry includes energy-intensive processes, e.g., clinker rotary kilns and clinker grate coolers. Clinker is obtained through chemical and physical reactions in a rotary kiln from raw meal; these reactions also involve combustion processes. The grate cooler is located downstream of the clinker rotary kiln with the purpose of suitably cooling the clinker. The clinker is cooled through the action of multiple cold air fan units as it is transported within the grate cooler. The present work describes a project where Advanced Process Control techniques are applied to a clinker rotary kiln and a clinker grate cooler. Model Predictive Control was selected as the main control strategy. Linear models with delays are obtained through ad hoc plant experiments and suitably included in the controllers' formulation. A cooperation and coordination policy is introduced between the kiln and the cooler controllers. The main objectives of the controllers are to control the rotary kiln and grate cooler critical process variables while minimizing the fuel/coal specific consumption of the kiln and the electric energy consumption of the cold air fan units within the cooler. The overall control system was installed on the real plant, obtaining significant results in terms of service factor and control and energy-saving performances.

Hybrid model of a cement rotary kiln using an improved attention-based recurrent neural network

A rotary kiln is core equipment in cement calcination. Significant time delay, time-varying, and nonlinear characteristics cause challenges in the advance process control and operational optimization of the rotary kiln. However, the traditional mechanism model with many assumptions cannot accurately represent the dynamic kiln process because kinetic parameters are difficult to obtain. This paper proposes a novel hybrid strategy to develop a dynamic model of a rotary kiln by combining a process mechanism and a recurrent neural network to address this issue. A time delay mechanism is used to estimate the kiln's residence time to compensate for the time delay. A long short-term memory model that combines an attention mechanism and an ordinary differential equation solver is proposed to capture the time-varying and nonlinear behaviors of the kiln process. Case studies from two real-world cement plants with different processing loads are used to verify the effectiveness of the proposed hybrid modeling strategy. The results show that the proposed method has better accuracy and robustness than the traditional methods. The sensitivity analysis of the proposed model also makes it practical for t control system design and real-time optimization.