Due to the nonlinearity in phase shifting profilometry (PSP) system, the captured images are often distorted with fringe harmonics, resulting in inaccurate phase map and measurement. Considering the fact that the phase error can be significantly reduced by modeling high-order fringe harmonics, this work formulates the phase extraction problem — with different frequency images and high-order fringe harmonic model — as a maximum likelihood estimation (MLE). To optimize it efficiently, we thus propose a combined-frequency phase extraction (CFPE) solution by introducing a latent phase map and incorporating the famous expectation-maximization (EM) framework. As a result, our CFPE method only needs ∼5% execution time of a high-order baseline, whilst keeps the high-order accuracy. Tested on synthetic images as well as practical measurements, our CFPE method demonstrated its performance improvement of efficiency and accuracy. In addition, our detailed implementation with experimental arrangement is also provided for interested researchers.

Dealing with municipal sludge in an effective way is crucial for urban development and environmental protection. Co-processing the sludge by burning it in a decomposition furnace in the cement production line has been found to be a viable solution. This work aims to analyze the effects of the co-disposal of municipal sludge on the decomposition reactions and NOx emissions in the decomposing furnaces. Specifically, a practical 6000 t/d decomposition furnace was taken as the research object. To achieve this, ANSYS FLUENT with a UDF (user-defined function) was applied to establish a numerical model coupling the limestone decomposition reaction, fuel combustion, and NOx generation and reduction reactions. The flow, temperature, and component field distributions within the furnace with no sludge were firstly simulated with this model. Compared with site test results, the model was validated. Then, with sludge involved, the structure and operation parameters of the decomposition furnace for the co-disposal of municipal sludge were investigated by simulating the flow field, temperature field, and component field distributions. Parametric studies were carried out in three perspectives, i.e., sludge mixing ratio, preheating furnace arrangement height, and sludge particle size. The results show that all three aspects have great importance in the discomposing process. A set of preferable values, including a sludge mixing ratio of 10%, preheating furnace height of 21.5 m, and sludge particle diameter of 1.0 mm, was obtained, which resulted in a raw material decomposition rate of 89.9% and a NO volume fraction of 251 ppm at the furnace outlet.

Spray cooling efficiency plays a critical role in the heat dissipation process from the external surface of industrial low-carbon cement rotary coolers. This study numerically investigated the thermal performance of high-temperature zones by examining four spray parameters: spray angle, nozzle distance, spray height, and mass flow rate. Multi-objective optimization design (MOD) was subsequently performed using response surface methodology (RSM). RSM reveals spray angle as the most significant parameter affecting heat transfer. With temperature uniformity as a constraint, MOD yields the following optimal parameters: 89° spray angle, 380 mm nozzle distance, and 663.5 mm spray height. This configuration achieves an average surface temperature of 814.33 K and a heat flux of 131,588.3 W/m². The optimized spray parameters ensure high heat flux and uniform surface temperature while enlarging the heat transfer area and strengthening the synergistic heat transfer between dual nozzles. This approach provides a reliable technical pathway for efficient thermal management in industrial rotary cooler exteriors.

The accurate reconstruction of the internal temperature field in rotary kilns is critical for optimizing the clinker calcination process and ensuring energy efficiency. In this study, a rapid and high-fidelity surrogate modeling framework is proposed, utilizing snapshot ensembles generated by full-order Computational Fluid Dynamics (CFD) simulations to reconstruct the temperature field of the axial center section. The framework incorporates a symmetric Autoencoder (AE) coupled with a TabPFN network as its core components. Capitalizing on the kiln’s strong axial symmetry, this reduction–regression system efficiently maps the high-dimensional nonlinear thermodynamic topology of the central section into a compact low-dimensional latent manifold via AE, while utilizing TabPFN to establish a robust mapping between operating boundary conditions and these latent features. By leveraging the In-Context Learning (ICL) mechanism for prior-data fitting, TabPFN effectively overcomes the data scarcity inherent in high-cost CFD sampling. Predictive results demonstrate that the model achieves a coefficient of determination (R²) of 0.897 for latent feature regression, outperforming traditional algorithms by 6.53%. In terms of field reconstruction on the test set, the model yields an average temperature error of 15.31 K. Notably, 93.83% of the nodal errors are confined within a narrow range of 0–50 K, and the reconstructed distributions exhibit high consistency with the CFD benchmarks. Furthermore, compared to the hours required for full-scale simulations, the inference time is reduced to 0.45 s, representing a speedup of four orders of magnitude. Consequently, the predictive system demonstrates excellent accuracy and efficiency, serving as an effective substitute for traditional models to realize online monitoring and intelligent optimization.

In the complex process of assembling biomass heating systems, traditional paper documents and construction process card management methods have weak information correlation and take a long time for information retrieval, which seriously restricts the assembly efficiency and quality. Moreover, the assembly process involves numerous components and complex processes, making it difficult for traditional management methods to cope with. To address this issue, a knowledge graph-based assembly information integration method is proposed to integrate scattered assembly information into a graph database, providing pathways for accessing assembly information and assisting on-site management. The biomass heating system assembly knowledge graph (BAKG) adopts the top-down method construction. After the construction of the upper schema layer, the 3DXML file was parsed, the XML.dom parser in Python3.7.16 was used to extract the equipment structure information, and the RoBERTa-BiLSTM-CRF model was applied to the named entity recognition of the assembly document, which improved the accuracy of entity recognition. The experimental results show that the F1 score of the RoBERTa-BiLSTM-CRF model in entity recognition during the assembly process reaches 92.19%, which is 3.1% higher than that of the traditional BERT-BiLSTM-CRF model. Moreover, the knowledge graph structure generated by the equipment structure data based on 3DXML file is similar to the equipment structure tree, but is more clear and intuitive. Finally, taking the second-phase construction process records of a company as an example, BAKG was constructed and assembly information was stored in the Neo4j graph database in the form of graphs, which verified the effectiveness of the method.