Skip to main content

Advertisement

SpringerLink
Log in

Multi-objective prediction for denitration systems in cement: an approach combining process analysis and bi-directional long short-term memory network

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Selective Non-Catalytic Reduction (SNCR) can improve the denitration process and reduce NOx emissions by accurizing prediction of NOx concentration and ammonia escape. However, there are inevitable time delays and nonlinearity problems in the prediction of NOx emission. To reduce NOx concentration quickly in SNCR, excessive ammonia spraying often causes a large amount of ammonia to escape, resulting in secondary pollution. Therefore, it is particularly important to monitor ammonia escape. To solve the above problems, this paper proposes a framework by specifically analyzing the cement denitration process and combining a multi-objective time series bi-directional long short-term memory network (MT-BiLSTM). Among them, the model achieves multi-objective prediction of NOx emission concentration and ammonia escape simultaneously. In addition, time series containing delay information are introduced in the input layer to eliminate the influence of delay. Based on the bi-directional LSTM model, the dropout strategy is adopted to improve the generalization of the model and the Adam optimizer is applied to improve the network performance. Besides, through the multi-step prediction of NOx emission at 3 time points, the dynamic nature of the data is preserved, which provides dynamic information support for realizing the automation of denitration system. The prediction performance of the MT-BiLSTM model is experimentally validated, and the results demonstrate that it can reliably predict both NOx and ammonia escape. The model achieves more accurate and reliable results for the prediction of flue gas concentrations compared with other methods such as SVR, DTR and LSTM. Therefore, the MT-BiLSTM model provides a basis for achieving NOx emission reduction and accurate ammonia injection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig.16
Fig. 17

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Almeshal A, Al-Jobory AA, Mijbil ZY (2022) Precise control of single-phenanthrene junction’s conductance. J Comput Electron 21:71–79

    Article  CAS  Google Scholar 

  • Almilaji O (2022) Modelling the episodes of care for iron deficiency anemia patients in a secondary-care center using continuous-time multistate Markov chain. Saudi Journal of Gastroenterology : Official Journal of the Saudi Gastroenterology Association 28:115–121

    Article  Google Scholar 

  • Cai J, Zheng W, Wang Q (2021) Effects of hydrogen peroxide, sodium carbonate, and ethanol additives on the urea-based SNCR process. Sci Total Environ 772

  • Cao Y, Yang F, Tang Q, Lu X (2019) An attention enhanced bidirectional LSTM for early forest fire smoke recognition. IEEE Access 7:154732–154742

    Article  Google Scholar 

  • Cao J, Wang X, Yuan Y, Zhang Z, Liu Y (2021) Multi-objective optimization of micro-fin helical coil tubes based on the prediction of artificial neural networks and entropy generation theory. Case Studies in Thermal Engineering 28

  • Chen G, Liu J, He G (2019) Research on application of limit learning method and least square support vector machine in ecological emission prediction. Fresen Environ Bull 28:9223–9229

    CAS  Google Scholar 

  • Chen C, Zhang Q, Kashani MH, Jun C, Bateni SM, Band SS, Dash SS, Chau K (2022a) Forecast of rainfall distribution based on fixed sliding window long short-term memory. Eng Appl Comp Fluid 16:248–261

    Google Scholar 

  • Chen T, Xiong Y, Chen Y, Chiang P, Chen Y (2020) Performance evaluation and process simulation for synergetic removal of NOx, CO2 and PM using green alkaline solution in a high-gravity rotating packed bed. FUEL 280

  • Chen Y, Chen X, Xu A, Sun Q, Peng X (2022b) A hybrid CNN-Transformer model for ozone concentration prediction. Air Quality, Atmosphere & Health

  • Cui J, Jia X, Hou P, Hu Y, Lei X (2019) Prediction of NOx generation process based on a nonlinear MA model. Proceedings of the 2019 31st Chinese control and decision conference (CCDC 2019), 230–235

  • de Azevedo ARG, Cruz ASA, Marvila MT, de Oliveira LB, Monteiro SN, Vieira CMF, Fediuk R, Timokhin R, Vatin N, Daironas M (2021) Natural fibers as an alternative to synthetic fibers in reinforcement of geopolymer matrices: a comparative review. Polymers-BASEL 13:2493

    Article  Google Scholar 

  • Farid AB, Fathy EM, Eldin AS, Abd-Elmegid LA (2021) Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM). PeerJ Computer Science 7

  • Graves A, Fernandez S, Schmidhuber J (2005) Bidirectional LSTM networks for improved phoneme classification and recognition. In: Duch W, Kacprzyk J, Oja E , Zadrozny S (Duch W, Kacprzyk J, Oja E , Zadrozny S)^(Duch W, Kacprzyk J, Oja E , Zadrozny Ss)|,*Lecture Notes in Computer Science, pp. 799–804

  • Hao X, Xu Q, Shi X, Song Z, Ji Y, Zhang Z (2021) Prediction of nitrogen oxide emission concentration in cement production process: a method of deep belief network with clustering and time series. Environ Sci Pollut R 28:31689–31703

    Article  CAS  Google Scholar 

  • Hao S, Yuling L, Yang J (2022) Construction of Cu-BTC by carboxylic acid organic ligand and its application in low temperature SCR denitration. The Science of the Total Environment 820:152984–152984

    Article  CAS  Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long-short term memory. Neural Comput

  • Huang M, Feng H, Li N, Shen D, Zhou Y, Jia Y (2017) Addition of large amount of municipal sewage sludge as raw material in cement clinker production. Environ Sci Pollut R 24:27862–27869

    Article  CAS  Google Scholar 

  • Huang X, Tang J, Yang X, Xiong L (2022) A time-dependent attention convolutional LSTM method for traffic flow prediction. Appl Intell

  • Hussain T, Muhammad K, Ullah A, Cao Z, Baik SW, de Albuquerque VHC (2020) Cloud-assisted multiview video summarization using CNN and bidirectional LSTM. IEEE T Ind Inform 16:77–86

    Article  Google Scholar 

  • Jimenez-Relinque E, Castellote M (2020) Rapid assessment of the photocatalytic activity in construction materials: pros and cons of reductive inks and oxidative fl uorescence probes versus standardized NOx testing. Catal Today 358:164–171

    Article  CAS  Google Scholar 

  • Jo H, Kang K, Park J, Ryu C, Ahn H, Go Y (2019) Optimization of air distribution to reduce NOx Emission and unburned carbon for the retrofit of a 500 MWe tangential-firing coal boiler. Energies 12

  • Kim HC, Bae C, Bae M, Kim O, Kim B, Yoo C, Park J, Choi J, Lee J, Lefer B, Stein A, Kim S (2020) Space-Borne Monitoring of NO(x)Emissions from Cement Kilns in South Korea. Atmosphere-BASEL 11

  • Krishan M, Jha S, Das J, Singh A, Goyal MK, Sekar C (2019) Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India. Air Qual Atmos Health 12:899–908

    Article  CAS  Google Scholar 

  • Li Y, Hu Z, He B, Chen Q (2021) Establishment of NOx concentration model at the outlet of SCR denitrification system for alkali recovery furnace flue gas based on improved LSSVM. Journal of Korea TAPPI 53:16–27

    CAS  Google Scholar 

  • Li Z, Zhang W, Chen Z, Zhang Q, Yang X, Mao S, Jian W (2022) Reaction mechanism for NO oxidation on the soot surface using a quantum chemistry. Fuel 313:123032

    Article  CAS  Google Scholar 

  • Mahjoubi S, Barhemat R, Guo P, Meng W, Bao Y (2021) Prediction and multi-objective optimization of mechanical, economical, and environmental properties for strain-hardening cementitious composites (SHCC) based on automated machine learning and metaheuristic algorithms. J Clean Prod 329

  • Meng Z, Wang C, Wang X, Chen Y, Wu W, Li H (2019) Simultaneous removal of SO2 and NOx from flue gas using (NH4)(2)S2O3/steel slag slurry combined with ozone oxidation. Fuel 255

  • Peng T, Zhang C, Zhou J, Nazir MS (2021) An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting. Energy 221:119887

    Article  Google Scholar 

  • Ramakrishna Balaji C, Azevedo ARGD, Madurwar M (2022) Sustainable perspective of ancillary construction materials in infrastructure industry: an overview. J Clean Prod 365:132864

    Article  Google Scholar 

  • Ronran C, Lee S (2020) Effect of character and word features in bidirectional LSTM-CRF for NER. 2020 IEEE international conference on big data and smart computing (BIGCOMP 2020), 613–616

  • Sahadevan D, M HP, Ponnusamy P, Gopi VP, Nelli MK (2022) Ground-based 4d trajectory prediction using bi-directional LSTM networks. APPL INTELL

  • Shen S, Atangana Njock PG, Zhou A, Lyu H (2021) Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning. Acta Geotech 16:303–315

    Article  Google Scholar 

  • Tamilvanan V, Subramani M, Subramani D, Ramasamy S (2022) Probing of sequential atmospheric degradation of chlorine radical initiated 1,8-cineole in the presence of O-2 and NO radical with the emission of secondary pollution. Environ Pollut 300

  • Vlachas PR, Pathak J, Hunt BR, Sapsis TP, Girvan M, Ott E, Koumoutsakos P (2020) Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics. Neural Netw 126:191–217

    Article  CAS  Google Scholar 

  • Wang Y, Xie R, Liu W, Yang G, Li X (2021) Modeling and optimization of NOx emission from a 660 MW coal-fired boiler based on the deep learning algorithm. J Chem Eng JPN 54:566–575

    Article  CAS  Google Scholar 

  • Wejkowski R, Kalisz S, Garbacz P, Maj I (2021) Combined NOx and NH3 slip reduction in a Stoker boiler equipped with the hybrid SNCR plus SCR system FJBS. ENERGIES 14

  • Wu H, Cai J, Ren Q, Shi C, Zhao A, Lyu Q (2020): A thermal and chemical fuel pretreatment process for NOx reduction from cement kiln. Fuel Process Technol 210

  • Wu X, Liu J, Wei Z, Chen Z, Evrendilek F, Huang W (2022) Oxy-fuel co-combustion dynamics of phytoremediation biomass and textile dyeing sludge: gas-to-ash pollution abatement. Sci Total Environ 825

  • Xiao X, Luo J, Huang H, Xu Z, Wu H, Huang J, Fang P (2020) Study on the influencing factors of removal of NO(X)from cement kiln flue gas by sewage sludge as a denitration agent. Environ Sci Pollut R 27:41342–41349

    Article  CAS  Google Scholar 

  • Yan J, Lu X, Zhang C, Li Q, Wang J, Liu S, Zheng X, Fan X (2021) An experimental study on the characteristics of NOx distributions at the SNCR inlets of a large-scale CFB boiler. Energies 14

  • Yang Y, Gao F, Li GY, Jian M (2019) Deep learning-based downlink channel prediction for FDD massive MIMO system. IEEE Commun Lett 23:1994–1998

    Article  Google Scholar 

  • Ye H, Lin H, Song Z, Zhang M, Hu R, Li N, Li G (2020) Attention bidirectional LSTM networks based mime speech recognition using sEMG data. 2020 IEEE international conference on systems, man, and cybernetics (SMC), 3162–3167

  • Ye M, Qian F, Gao Y, Lu J, Han Y, Huang N, Xu B, Wu H (2021) CFD analysis of influencing factors on SCR denitration efficiency of sintering flue gas based on response surface methodology. Atmos Pollut Res 12

  • Yin C, Huang X, Cao J, Dadras S, Shi A (2021) Infrared feature extraction and prediction method based on dynamic multi-objective optimization for space debris impact damages inspection. J Franklin I 358:10165–10192

    Article  Google Scholar 

  • Zeng J, Bao R, McFarland M (2022) Clean energy substitution: the effect of transitioning from coal to gas on air pollution. Energ Econ 107

  • Zhai Y, Ding X, Jin X, Zhao L (2020) Adaptive LSSVM based iterative prediction method for NOx concentration prediction in coal-fired power plant considering system delay. Appl Soft Comput 89

  • Zhang W, Zhang Y, Bai X, Liu J, Zeng D, Qiu T (2016) A robust fuzzy tree method with outlier detection for combustion models and optimization. Chemometr Intell Lab 158:130–137

    Article  CAS  Google Scholar 

  • Zhao Y, Feng J, Chen Y, Fan H, Bai W (2021) Thermal process and NO emission reduction characteristics of a new-type coke oven regenerator coupled with SNCR process. FUEL 305

  • Zhen C, Liu H (2019) Model for predicting NOx emission from boilers based on MWOA-LSSVM integration. J CHEM ENG JPN 52:702–709

    Article  CAS  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 62073281), the Hebei Provincial Natural Science Foundation (Grant No. F2022203088), the Hebei Provincial Science and Technology Plan Project (Grant No. 19211602D).

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Yanshan University, 438 Hebei Avenue, Qinhuangdao, 066004, China

    Xiaochen Hao, Yinlu Di, Qingquan Xu, Pengfei Liu & Wang Xin

Authors
  1. Xiaochen Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yinlu Di
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qingquan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pengfei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wang Xin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Xiaochen Hao: Conceptualization, Formal analysis, Funding acquisition Supervision.

Yinlu Di: Writing—Original Draft, Visualization, Data Curation, Software.

Qingquan Xu: Project administration, Software, Methodology.

Pengfei Liu: Writing—Review & Editing.

Xin Wang: Data Curation, Software, Data Curation.

Corresponding author

Correspondence to Xiaochen Hao.

Ethics declarations

Ethics approval and consent to participate

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent for publication

The authors have given their consent for publication.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Guilherme L. Dotto

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hao, X., Di, Y., Xu, Q. et al. Multi-objective prediction for denitration systems in cement: an approach combining process analysis and bi-directional long short-term memory network. Environ Sci Pollut Res 30, 30408–30429 (2023). https://doi.org/10.1007/s11356-022-24021-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-022-24021-5

Keywords

Search

Navigation