Prediction Of Emissions Research Articles

Understanding soil and ecosystem respiration in a dune-meadow cascade ecosystem

Arid and semi-arid regions, which account for more than 30% of the Earth's land area, increasingly dominate the spatiotemporal trends in global carbon fluxes. The Horqin Sandy Land is a typical semi-arid fragile ecosystem in northern China. Understanding the components of the carbon budget in ecosystems under conditions of extreme soil moisture limitations provides a foundation for comprehending the carbon balance in semi-arid ecosystems. The seasonal and diurnal variations in soil respiration (Rs) in semi-mobile dune (SD) and meadow wetland (MW) ecosystems of the Horqin Sandy Land were examined, and the sources of CO2 emissions from Rs were identified using stable carbon isotopes. The responses of Rs and ecosystem respiration (Reco) to environmental temperature, moisture and leaf area index (LAI) were revealed. The results showed that on a seasonal scale, in SD with soil moisture content (Ms) below field capacity (FC), Ms had a greater influence on Rs than soil temperature (Ts) during the growing season. Changes in the LAI during the middle and late growth period affected Rs by altering root carbon supply. In MW, the most favorable Ms for Rs was near FC. The increase in LAI before mowing could effectively promote root and soil microbial respiration, and the decomposition of litter driven by Ts was the main form of Rs at this time. After mowing, root respiration and soil microbial respiration were the main processes contributing to CO2 emissions. On a daily scale, relative humidity (RH) dominated the Rs variation under dry conditions, whereas in other conditions, the Rs was adequately explained by temperature in SD and MW. The overall Reco was larger than Rs, but occasionally Rs was greater than Reco. The effects of temperature, moisture and LAI on Reco and Rs varied with growing season. Adding factors, such as ecosystem type, vegetation growth, water, and heat, to the carbon cycle model can improve predictions of carbon emissions, and aid in further management decisions in arid and semi-arid areas.

Optimization of ANN Models Using Metaheuristic Algorithms for Prediction of Tailpipe Emissions in Biodiesel Engine

ABSTRACTMachine learning techniques are gaining momentum in the present‐day context in most engineering applications due to their versatility and accuracy. They facilitate faster data processing coupled with a high degree of accuracy. They are extensively used in understanding and modeling engine combustion and emissions. Engine emissions significantly contribute to environmental degradation. In the current study, an effort has been made to compare the emissions recorded from a four‐stroke single‐cylinder biodiesel engine with those obtained using artificial neural network (ANN) models, where the hyperparameters have been optimized using nature‐inspired metaheuristic optimization algorithms like JAYA, WOA, ROA, and WaOA. The study was conducted using diesel and cardanol‐methanol‐diesel blends of B10M10, B20M10, and B30M10, by varying the fuel injection pressure from 180 bar (standard injection timing) to 220 bar with an interval of 20 bar. Furthermore, experiments were conducted with oxygen enrichment at concentrations of 3%, 5%, and 7% w/w on the standard oxygen concentration of air. The study showed a remarkable reduction of 59% in CO emissions at 220 bar fuel injection pressure with 7% w/w oxygen enrichment for the B30M10 blend as compared to 180 bar without oxygen enrichment. A similar reduction of 32.6% and 16.6% were observed for HC emissions and smoke opacity for the same operating conditions. However, a rising trend of 50% was observed for NOx emissions for the same blend and operating conditions. The findings indicate that the data recorded conforms with that obtained by using the ANN model optimized through these metaheuristic algorithms.

The Global Forest Fire Emissions Prediction System version 1.0

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in near-real time for global air quality forecasting. The model uses a bottom-up approach, based on remotely sensed hotspot locations, and global databases linking burned area per hotspot to ecosystem-type classification at a 1 km resolution. Unlike other global fire emissions models, GFFEPS provides dynamic estimates of fuel consumption, fire behaviour and fire growth based on the Canadian Forest Fire Danger Rating System, plant phenology as calculated from daily global weather and burned-area estimates using near-real-time Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-detected hotspots and historical burned-area statistics. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3 h time steps (in contrast to non-dynamic models that use fixed consumption rates and require a collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in operational forecasting applications as well as historical simulations for which data are available. A study was conducted showing GFFEPS predictions through a 6-year period (2015–2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model's fuel consumption per unit area results clearly distinguished regions dominated by grassland (Africa) from those dominated by forests (boreal regions) and showed high variability in regions affected by El Niño and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including the Global Fire Assimilation System (GFAS), the Global Fire Emissions Database (GFED4.1s) and the Fire INventory from NCAR (FINN 1.5 and 2.5). GFFEPS estimated values lower than GFAS and GFED (80 % and 74 %) and had values similar to FINN 1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. Model evaluation efforts to date are described – an ongoing effort is underway to further validate the model, with further developments and improvements expected in the future.

ANALYSIS OF THE EFFECT OF SWIRL FLAME SHAPING ON EMISSIONS FROM THE CO-FIRING OF AMMONIA AND METHANE

This paper presents experimental and numerical insights into the behaviour of lean swirl flames with co-combustion of ammonia and methane. In order to evaluate the accuracy of the emissions modelling for CH4/NH3/air preheated (473K) mixtures with an increasing share of ammonia, experimental investigations were performed, varying the share of ammonia up to 25% in the fuel and the equivalence ratio of air to fuel of 0.71. A burner featuring swirl outflow blades with a 30-degree angle of outflow and a convergent-divergent nozzle was employed in the experimental setup. In order to determine the size of the reaction zone, the concentrations of NO and CO along the probing radius inside the combustion chamber were measured. A complete geometry of the combustion chamber and burner was modelled with RSM and EDC models. The outcomes of the Okafor, Xiao, and San Diego mechanisms were compared to the test data. A further DES simulation was conducted to assess the potential applicability of the models. For the flames tested with 3D RSM, a good agreement was achieved for NO emissions. In the case of a fuel containing between 10% and 25% ammonia, the Okafor mechanism demonstrated the most favourable qualitative and quantitative agreement. The reactor volume, residence time and heat transfer conditions for the CRN reactor network were calculated using numerical CFD methods. A satisfactory correlation was observed between the calculated and experimental NO emissions, with a discrepancy of only 10%. In view of the considerable computational expense associated with the models employed and the high degree of accuracy demonstrated in the emission predictions, the 3D RSM simulation was deemed to be an appropriate means of modelling small-scale applications.

Learning rule in MFR pulse sequence for behavior mode prediction

Radar behavior prediction is an important task in the field of electronic reconnaissance. For the extensive applied multi-function radar (MFR), which can flexibly transition between various work modes and make certain statistical rule of these radar behaviors exist in the signal sequence. Most of existing radar emission prediction methods are inapplicable to the non-cooperative scenario, since the labeled sequence samples are hard to obtain. To solve this challenge, an unsupervised framework is proposed for learning the behavior rule from the pulse sequence and predicting the radar mode in this paper. The framework includes three modules of sequence segmentation for mode switch boundaries detection, segment clustering for behavior mode recognition, and mode prediction for behavior rule extraction. The application of this framework can predict state and numerical values of next mode at the same time. Experimental results demonstrate that the proposed framework has a considerable prediction performance and shows good robustness under the non-ideal conditions.

Multi-Scenario land cover changes and carbon emissions prediction for peak carbon emissions in the Yellow River Basin, China

Research on future land cover changes and carbon emissions is essential for effective land resource management and developing feasible carbon mitigation strategies. This study focused on the Yellow River Basin and employed the Future Land Use Simulation (FLUS) and Autoregressive Integrated Moving Average (ARIMA) models to project future land cover and carbon emissions. Additionally, bivariate spatial autocorrelation was utilized to analyze the relationship between them. Key findings are as follows: 1) Historically, the Yellow River Basin has experienced an expansion in construction land, forests, grasslands, and water, while cropland and unused land have diminished. Notably, construction land displayed the most significant changes, whereas grasslands showed minimal modification. Looking ahead, both the ecological protection and inertial development scenarios exhibit consistent trends with historical patterns across the land type categories. In contrast, the economic priority development scenario forecasts an increase in construction land, cropland, and grasslands, indicating a distinct shift compared to the other scenarios. However, the ecological protection scenario proves to be more sustainable. 2) In the absence of intervention, the simulated carbon emissions from construction land throughout the basin display a linear increase across various scenarios, with provincial-level variations showing an increase from southwest to northeast. However, Henan and Sichuan are expected to experience slower reductions in carbon emissions, compared to other projections. There is a notable positive correlation between carbon emissions and the comprehensive index, indicating that regions with high emissions typically experience substantial land and economic development. 3) Energy consumption projections for 2030 and 2060 indicate that to align with China’s carbon goals, it is essential to reduce energy consumption and adjust the fossil to non-fossil fuel ratio to reduce carbon emissions. Substituting coal with clean energy and enhancing energy efficiency will be more effective for achieving low-carbon emission targets. In summary, this study provides significant guidance for China’s ecological conservation, low-carbon emission strategies, and global carbon emission control efforts.

Prediction of Energy-Related Carbon Emissions in East China Using a Spatial Reverse-Accumulation Discrete Grey Model

In order to better analyze and predict energy-related carbon emissions in East China to address climate change, this paper enhances the predictive capabilities of grey models in spatial joint prediction by creating the reverse-accumulation spatial discrete grey model RSDGM (1,1,m) and accumulation spatial discrete grey breakpoint model RSDGBM (1,1,m,t), which took the impact of system shocks into consideration. The efficiency of the models is confirmed by calculating the energy-related carbon emissions in East China from 2010 to 2022. Future emissions are predicted, and the spatial spillover effect of emissions in East China is discussed. The conclusions are as follows: (1) The RSDGM (1,1,m) theoretically avoids errors in background values and parameter calculations, reducing computational complexity. Empirically, the model exhibits high performance and reflects the priority of new information in spatial joint analysis. (2) The RSDGBM (1,1,m,t) captures the impact of shocks on system development, improving the reliability of carbon emissions prediction. (3) Jiangsu and Shandong are positively affected by spatial factors in terms of carbon emissions, while Shanghai and Zhejiang are negatively affected. (4) It is estimated that carbon emissions in East China will increase by approximately 23.8% in 2030 compared to the level in 2022, with the levels in Zhejiang and Fujian expected to increase by 45.2% and 39.7%, respectively; additionally, the level in Shanghai is projected to decrease. Overall, East China still faces significant pressure to reduce emissions.

Optimization study of diesel engine emission prediction based on neural network model cluster

This study aims to reduce the testing volume and cost of engine bench tests. By combining neural network models and ensemble learning algorithms, a model cluster prediction method is proposed to predict engine emissions. The core of this method is to use a large number of neural network models for prediction, taking the average of the prediction results as the final prediction result, thereby reducing prediction errors and improving accuracy. The findings reveal that this cluster-based approach significantly outperforms a single optimal model in forecasting steady-state emissions of NOx, HC, and CO in diesel engines. Notably, the performance of the model cluster is highly dependent on both the quality and the number of constituent sub-models, with enhanced predictive capabilities observed when higher-quality sub-models are included. Additionally, the study identifies a stabilization in predictive accuracy as the number of models increases, particularly within the range of 50 to 100 models, and achieving robust stability beyond 100 models. The research also underscores the importance of the training dataset size on the effectiveness of neural network modeling. A reduction in the training dataset from 28% to 18% leads to a decline in the coefficient of determination ( R2) for NOx emissions prediction in a single neural network model from 0.6954 to 0.6539, a 5.97% decrease. For the model cluster method, the R2 similarly drops from 0.8633 to 0.8154, a reduction of 5.55%. Notably, the neural network model suffers from distortion in predicting the peak position of NOx emissions. After supplementing specific operating condition training data in a targeted manner, the model training amount was increased from 18% to 25%. The model cluster method showed a good improvement in the prediction of NOx emissions, with the prediction coefficient increasing from 0.8154 to 0.8997, with an improvement rate of 10.34%. Through planning of training data, the study demonstrates that employing just 25% of experimental data for model clustering can effectively predict the engine emission trends for the remaining 75% of data in the target conditions. This approach offers a substantial reduction in experimental requirements while maintaining high prediction accuracy.

Research on China's Carbon Emission Prediction and Low-Carbon Development Strategy Based on Big Data

Research on China's Carbon Emission Prediction and Low-Carbon Development Strategy Based on Big Data

An adaptive prediction method for carbon emissions of power systems containing new energy based on least-squares support vector machine

Aiming at the problem of high volatility and intermittency of new energy-containing power systems, which affects the prediction accuracy of carbon emissions, we study the adaptive prediction method of carbon emissions of a new energy-containing power system based on a least-squares support vector machine. The carbon emission coefficients of the nodes of the new energy-containing power system are determined based on the trend analysis method. Historical carbon emissions and carbon emission coefficients of the power system are collected, and the rough set method is used to simplify the carbon emission attributes and obtain a simplified attribute set for carbon emission prediction. The least-squares support vector mechanism is used to construct the carbon emission prediction model, and the simplified attribute set is used as the input of the carbon emission prediction model. The whale optimization algorithm was selected to determine the optimal parameters of the least-squares support vector machine through the process of encircling prey, hunting behavior, searching for predation, and adaptively optimizing the least-squares support vector machine to output carbon emission prediction results. The experimental results show that the method can accurately predict the carbon emissions of the power system, and the mean square error of the carbon emissions prediction is lower than 7.

Advanced hybrid neural network techniques for minimizing gas turbine emissions

Purpose Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS). Design/methodology/approach The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies. Findings The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R2 of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R2 of 0.887 and a slight overestimation bias of +0.01. Research limitations/implications While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research. Practical implications Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs. Social implications Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment. Originality/value This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.

Emissions and Flame Stability Assessment of Hydrogen Addition and Air Dilution in a Micro Gas Turbine Combustor Using 0D/1D Modeling by Chemical Reactor Network

Abstract Hydrogen emerges as a promising fuel for clean and sustain- able electricity production when utilized in micro gas turbines (mGTs). However, some challenges linked to hydrogen usage must be overcome as its high reactivity can lead to increased NOx emissions and flame instabilities. Literature suggests that combustion air humidification and/or exhaust gas recirculation (EGR) may be methods to reduce this reactivity. However, these measures are limited due to the small operating range of the mGT combustor. In this context, a 20 kWth mGT combustor is investigated un- der various inlet conditions to assess the effect of EGR towards increased flame stability and emission control when operating under hydrogen-enriched methane firing, using a Chemical Re- action Network (CRN) model. The CRN modeling is a fast and low computational complexity tool to model combustion perfor- mance and emissions by representing complex reactive flow fields through idealized reactor models, leading to reduced computa- tional costs. This study examines partial load conditions, fuel compositions, and the effect of combustion air dilution through EGR. The CRN model of the combustion process is designed based on combustion zones extracted from the main flow fields with similar thermo-chemical states from CFD, while emission predictions are experimentally validated for different operating points. Predicted NOx and CO emissions by the proposed CRN model show an acceptable agreement with the experimental data at high power loads. However, its accuracy diminishes for loads below 70% due to the variability in model tuning parameters across different loads, whereas the

Measurement report: In-flight and ground-based measurements of nitrogen oxide emissions from latest-generation jet engines and 100 % sustainable aviation fuel

Abstract. Nitrogen oxides, emitted from air traffic, are of concern due to their impact on climate by changing atmospheric ozone and methane levels. Using the DLR research aircraft Falcon, total reactive nitrogen (NOy) in-flight measurements were carried out at high altitudes to characterize emissions in the fresh aircraft exhaust from the latest-generation Rolls-Royce Trent XWB-84 engine aboard the long-range Airbus A350-941 aircraft during the ECLIF3 (Emission and CLimate Impact of alternative Fuels 3) experiment. The impact of different engine thrust settings, monitored in terms of combustor inlet temperature, pressure and engine fuel flow, was tested for two different fuel types: Jet A-1 and, for the first time, a 100 % sustainable aviation fuel (SAF) under similar atmospheric conditions. In addition, a range of combustor temperatures and an additional blended SAF were tested during ground-based emission measurements. For the data measured during ECLIF3, we confirm that the NOx emission index increases with increasing combustion temperature, pressure and fuel flow. We find that as expected, the fuel type has no measurable effect on the NOx emission index. These measurements are used to compare to cruise NOx emission index estimates from three engine emission prediction methods. Our measurements thus help to understand the ground to cruise correlation of current engine emission prediction methods while serving as input for climate modelling and extending the extremely sparse data set on in-flight aircraft nitrogen oxide emissions to newer engine generations.

Spatiotemporal prediction of greenhouse gas emissions from rubber wood industry, taking Hainan as the case

The rubber wood furniture industry chain, including planting and manufacturing subsystems, emits significant greenhouse gases (GHGs), meanwhile, has huge carbon emission reduction potential. However, quite few studies have taken the whole industry chain GHGs into consideration, especially assessing and predicting those from rubber tree planting to furniture manufacturing. We took the rubber wood furniture of Hainan province as the case, explored its whole life cycle GHGs from cradle-to-gate and predicted the potential GHGs from 2031 to 2062. This study is fully based on field survey and sampling data, including that of the rubber tree planting (40-year cycle) and furniture manufacturing. The results reveal: (1) Whole industry chain of rubber wood furniture emits 3.27 × 106 kgCO2-eq GHGs per 20,000 t rubber logs consuming, where the planting accounts the most (88.20%). Transport and electricity consumption are the main GHGs sources of planting and manufacturing subsystems respectively; (2) The whole life-cycle GHGs of rubber wood furniture in 2023 of Hainan is 2.07 × 107 kgCO2-eq, which mainly come from Danzhou (24.74%), Haikou (19.55%), and Qiongzhong (15.79%); (3) The total GHGs from the rubber wood furniture industry in Hainan from 2031 to 2062 are 210 MtCO2-eq and 129 MtCO2-eq respectively, without or with felling restriction. The changes of GHGs from 2051 to 2062 are caused by the regional center shift of planting gravity; (4) Improving energy use efficiency and optimizing the location of furniture factories would reduce GHGs. This study can provide empirical support for the low carbon sustainable transformation of the rubber wood furniture industry worldwide.

Spatio-temporal characteristics and scenario prediction of carbon emissions from land use in Jiangxi Province, China

Spatio-temporal characteristics and scenario prediction of carbon emissions from land use in Jiangxi Province, China

Zone-based many-objective building decarbonization considering outdoor temperature and occupation uncertainty

Operational building decarbonization is challenging due to complex weather conditions and occupation uncertainty. This paper introduces a precise optimization framework integrating the building information modelling technique and intelligent algorithms to dynamically predict multi-scenario building carbon emissions and optimize the building emissions performance considering building zones and weather conditions. Firstly, the on-site data and building information modeling-supported building emissions simulation data are collected for model training. Secondly, using intelligent algorithms, zone-based hourly carbon emissions and multi-scenario carbon emissions prediction are conducted simultaneously. Thirdly, the optimal decarbonization strategies are conducted using intelligent algorithms under various weather conditions. This framework has been verified for decarbonization in a high-rise operational building. The results reveal that: (1) The carbon emissions prediction is highly consistent with the ground truth after considering the sub-zone correlations; the R2 for the west, south, and east zones are 0.900, 0.900, and 0.942, respectively; (2) The surrogate models can accurately predict carbon emissions and thermal comforts with all R2 are higher than 0.912. The optimization rate of the building reaches 59.2 % while the outdoor temperature is above 35 °C. (3) In the many-objective optimization model, considering occupation uncertainty makes the strategy close to the actual situation, reaching the decarbonization by 6815.23 kg compared to the empirical operation for the cooling period. This work provides a new path for operational building precise management and control-oriented optimization decarbonization.

Research on the Carbon Emission Prediction and Reduction Strategies for the Civil Aviation Industry in China: A System Dynamics Approach

With the continuous growth in the volume of global air transportation, the carbon emissions of the civil aviation industry have received increasing attention. Carbon emission reduction in civil aviation is an inevitable requirement for achieving sustainable social development. This article aims to use system dynamics (SD) methods to establish a carbon emission model for the civil aviation industry that includes economic, demographic, technological, policy, and behavioral factors; analyze the key factors that affect carbon emissions; and explore effective emission reduction strategies. Researchers have found that SD-based carbon emission prediction has a high accuracy and is suitable for predicting carbon emissions in civil aviation. Through different scenario simulations, it has been found that any single emission reduction measure will struggle to effectively contribute to the expected carbon reductions in China’s civil aviation. Simultaneously adopting measures such as improving fuel efficiency, adopting clean energy, and using new-power aircraft is an effective way to reduce carbon emissions from civil aviation. In addition, policy intervention and technological innovation are equally crucial for achieving long-term emission reduction goals. The research results not only provide a scientific basis for the sustainable development of the aviation industry but also provide a reference for policymakers to formulate comprehensive emission reduction strategies.

Influencing factors and predictions of carbon emissions for the chemical industry in China

As global warming increases the frequent occurrences of natural disasters, the reduction of carbon emissions has become an important issue around the world. The chemical industry is an important source of carbon emissions in China. The carbon emissions of the chemical industry are calculated from 2000 to 2019 by using the emission factor method. The logarithmic mean divisia index (LMDI) method is exploited to analyze the factors that influence carbon emissions, and the emissions variations are attributed to the contributions of carbon intensity, energy structure, energy intensity, industrial value-added rate, per capita industrial output value, and industrial scale. The results of decomposition show that per capita industrial output value is the main driving factor, and energy intensity is the main inhibiting factor of the chemical industry’s carbon emissions. In order to quantify the variation of carbon emissions, the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model is constructed and examined. Using the STIRPAT model, the basic scenario and energy intensity control scenario are set, and the carbon emissions are predicted, which shows that under a strict energy intensity control scenario, carbon emissions may reach a peak around 2031. The factors influencing the decomposition and prediction of carbon emissions should be helpful in reducing the carbon emissions of the chemical industry in China.

Study on prediction model of nitrogen emission in the production stage of residential building materials—A case study of Guangdong province

This study aims to reduce nitrogen emissions from residential buildings and establishes a prediction model for nitrogen emissions during the production of building materials. The calculation boundary, content, and method of nitrogen emission in the production stage of residential building materials are accurately analyzed. Based on the nitrogen emission data of 20 residential buildings in Guangdong, the composition and distribution characteristics of nitrogen emission in the production stage of building materials are analyzed. The coupling relationship between building design parameters and building materials’ nitrogen emissions is established using linear regression and ridge regression. 10 kinds of nitrogen emission prediction models based on the design parameters of residential buildings in the production stage of building materials were established and verified. The results show that the linear model M3, based on the number of floors above and below ground, the area width and depth, and the ridge regression model M5, based on the number of floors above and below ground, the area width and depth, and the total number of main functional rooms, have good fitting and prediction performance, respectively. The linear regression model M6, based on the number of floors above and below ground, the area width and depth, and the total number of rooms, has the best fitting and prediction performance. M3, M5, and M6 can accurately predict the nitrogen emission composition and distribution characteristics of building materials in residential building design and lay a foundation for future research on nitrogen emission evaluation and calculation methods and nitrogen emission reduction technology strategies.

Intelligent prediction of surface roughness of PSZ ceramic grinding based on correlation analysis and CNN-BiLSTM neural network

Partially stabilized zirconia (PSZ) ceramics are widely used in aerospace industry and other fields due to their superior performance. Surface roughness is a key indicator for evaluating the grinding level of PSZ ceramics. In order to reduce the prediction error of grinding surface roughness, an acoustic emission prediction model for PSZ ceramic grinding surface roughness based on correlation analysis and convolution-bidirectional long short term memory neural network (CNN-BiLSTM) was proposed. By analyzing the correlation between the eigenvalues of grinding acoustic emission signals and the grinding surface roughness values, the most relevant frequency bands and feature matrices between the grinding acoustic emission signals and the grinding surface roughness were selected as the input parameters of the CNN-BiLSTM neural network to reduce the error of acoustic emission prediction of grinding surface roughness. The research results show that the average prediction error of PSZ ceramic grinding surface roughness based on correlation analysis and CNN-BiLSTM neural network is less than 3.92%.