Journal Vol – 18 No -12, December 2023

Abstract:

In this study, a series of 1-D and steady-state numerical simulations have been performed for the prediction of the effect of the addition of H₂ on the characteristics of a non-sooting counterflow CH₄/Air diffusion flame using detailed chemical reaction model, which is composed of 325 elementary chemical reactions and 53 chemical species. Under the steady-state assumption, a set of one-dimensional transport equations of mass, momentum, species, and energy along with the equation of state has been solved numerically at the atmospheric conditions over the counterflow configuration by exploiting an efficient numerical code, OPPDIF (a Fortran Program for Computing Opposed-Flow Diffusion Flames). The grid adaption technique has been used to achieve better convergence as well as to ensure the maximum accuracy of the simulated results. It is found that the flame temperature is increased due to the addition of H₂ with CH₄, which is injected into the fuel stream. The elevation in the temperature is caused by the augmentation of the integrated heat release rate of the elementary reactions supported by the active radicals (H, O, and OH), which are generated by the higher reactivity of H₂. Besides, it is found that the mole fractions of H₂O are increased as the percentage of H₂ in the loading fuel (CH₄) is increased and also, it is identified that the chain propagating reaction, OH + H₂ => H₂O + H is dominating one which produces highest amount of H₂O. Furthermore, it is noticed that the indirect greenhouse gas or precursor, CO is reduced when H₂ is added to CH₄. Consequently, the mole fraction of the principle greenhouse gas, CO₂ is decreased significantly when the fuel, CH₄ percentage is modified by the higher percentage of H_2. The sensitivity analysis of elementary reactions reveals the fact that the chemical reaction: OH + CO => H + CO₂ is a dominating reaction in producing a lower amount of CO₂ when the volume fraction of H₂ is increased in the fuel (CH₄) stream. In the presence of 75 % H₂ in CH₄, the pressure-dependent reaction, O + CO (+M) => CO₂ (+M) appears as another chemical route that also generates greenhouse gas, CO₂ but its contribution is negligibly small.

Keywords:

Numerical simulation,Methane,Counterflow diffusion flame,Green fuel (H2),Greenhouse gas (CO2),

Refference:

I. Deng, S. Mueller, M. E., Chan, Q. N., Qamar, N. H., Dally, B. B., Alwahabi, Zeyad, T., Nathan, G. J., : ‘Hydrodynamic and chemical effects of hydrogen addition on soot evolution in turbulent nonpremixed bluff body ethylene flames.’ Proc. Combust. Inst., 36 (1), 807-814 (2017). 10.1016/j.proci.2016.09.004
II. Granata S, Faravelli T, Ranzi E, Nesrin, O., Selim, S., : ‘Kinetic Modeling of counterflow diffusion flames of butadiene.’ Combust. and Flame, 131, 273–284 (2002). 10.1016/S0010-2180(02)00407-8

III. Guo, H., Liu, F., Smallwood, G. J., : ‘A numerical study of the influence of hydrogen addition on soot formation in a laminar counterflow ethylene/oxygen/nitrogen diffusion flame.’ ASME Int. Mechanical, 10.1115/IMECE2004-59407

IV. Hossain A., Nakamura Y., : ‘A numerical study on the ability to predict the heat release rate using CH* chemiluminescence in non-sooting counterflow diffusion flames.’ Combust. Flame, 161, 162–172 (2014). 10.1016/j.combustflame.2013.08.021
V. Kenneth K. Kuo, Ragini Acharya, : ‘Fundamentals of Turbulent and Multiphase Combustion.’ John Wiley & Sons, Inc. (2012). 10.1002/9781118107683

VI. Liu, F., Smallwood, G. J., Gülder, Ö. L., : ‘Numerical study on the influence of hydrogen addition on soot formation in a laminar ethylene–air diffusion flame.’ Combust. and Flame, 145 (1-2), 324-338 (2006). 10.1016/j.combustflame.2005.10.016

VII. Lutz, A., Kee R. J., Grcar, Rupley, : ‘A Fortran Program Computing opposed flow diffusion flame.’ SAND96-8243, Sandia National Laboratories, Livermore, CA, USA, (1997). 10.2172/568983
VIII. Miao, J., Leung, C.W., Cheung, C. S. Huang, Z.H., Zhen, H.S., : ‘Effect of hydrogen addition on overall pollutant emissions of inverse diffusion flame.’ Energy, 104 (1), 284 – 294, (2016). 10.1016/j.energy.2016.03.114

IX. Pandya, T. P., Srivastava, N. K., : ‘Counterflow diffusion flame of ethyl alcohol.’ Combust. Sci. Tech., 5, 83–88 (1972). 10.1080/00102207208952507

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XII. Sun, C. J, Sung, C. J, Wang H, Law, C. K., : ‘On the structure of non-sooting counterflow ethylene and acetylene diffusion flames.’ Combust. and Flame, 107, 321–335 (1996).

XIII. Tsuji, H. : ‘Counter flow diffusion flames. Prog. Energy.’ Combust Sci., 8, 93–119(1982)

XIV. Wang, Y., Liu, X., Gu, M., Xueliang, : ‘A. Numerical simulation of the effects of hydrogen addition to fuel on the structure and soot formation of a laminar axisymmetricco-flow C2H4/(O2-CO2) diffusion flame.’ Combust. Sci. Tech., 191 (10), 1743-1768 (2019).
XV. Yadav, V. K., Yadav, J. P., Ranjan, P., : ‘Numerical and experimental
investigation of hydrogen enrichment effect on the combustion characteristics of biogas.’ Int. journal of renewable energy research, Vol.8 (3) September, 2018. 10.20508/ijrer.v8i3.7562.g7426
XVI. Zhu, Y., Wu, Jiajia, Zhu, B., Wang, Y., Gu, M., : ‘Experimental study on the effect of hydrogen addition on methane/ethylene diffusion flame soot formation based on light extinction measurement.’ Energy Reports, 7, 673-683 (2021). 10.1016/j.egyr.2021.09.203
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XX. https://www.ipcc.ch/report/ar3/wg1/chapter-4.: Atmospheric Chemistry and Greenhouse Gases.

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Abstract:

The objective of this work is to study the optimal policy of the classical economic production quantity (EPQ) model under interval uncertainty using interval inequality. To serve this purpose existing arithmetic mean-geometric mean (AM-GM) inequality is extended for interval numbers using c-L interval order relation. Then, using the said AM-GM interval inequality, the optimal policy of the classical EPQ model in the interval environment is developed.  Thereafter, the optimality policy of the classical EPQ model in a crisp environment is obtained as a special case of that of the interval environment. Finally, all the optimality results are illustrated with the help of some numerical examples.

Keywords:

Interval order relation,Generalised AM-GM inequality,c-L minimizer,Interval EPQ,c-L optimal policy,

Refference:

I. Bhunia, A. K., & Samanta, S. S., : ‘A study of interval metric and its application in multi-objective optimization with interval objectives.’ Computers & Industrial Engineering, 74, (2014), 169-178. 10.1016/j.cie.2014.05.014
II. Cardenas-Barron L. E., : ‘The economic production quantity (EPQ) with shortages derived algebraically.’ International Journal of Production Economics, 70, (2001), 289-292. 10.1016/S0925-5273(00)00068-2
III. Das, S., Rahman, M. S., Shaikh, A. A., Bhunia, A. K., & Ahmadian, A., : ‘Theoretical developments and application of variational principle in a production inventory problem with interval uncertainty.’ International Journal of Systems Science: Operations & Logistics, (2022), 1-20. 10.1080/23302674.2022.2052377
IV. Gani, A. N., Kumar, C. A., & Rafi, U. M., : ‘The Arithmetic Geometric Mean (AGM) inequality approach to compute EOQ/EPQ under Fuzzy Environment.’ International Journal of Pure and Applied Mathematics, 118(6), (2018), 361-370.
V. Grubbstrom. R. W., : ‘Material requirements planning and manufacturing resource planning.’ in: Warner. M (Ed.) International Encyclopaedia of Business and Management, Vol.4 Routledge, London, (1996), 3400-3420.
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VII. Manna, A. K., Rahman, M. S., Shaikh, A. A., Bhunia, A. K., & Konstantaras, I., : ‘Modeling of a carbon emitted production inventory system with interval uncertainty via meta-heuristic algorithms.’ Applied Mathematical Modelling, 106, (2022), 343-368. 10.1016/j.apm.2022.02.003
VIII. Moore, R. E., Kearfott, R. B., & Cloud, M. J., : ‘Introduction to interval analysis.’ Society for Industrial and Applied Mathematics.
IX. Rahman, M. S., Shaikh, A. A. & Bhunia, A. K., : ‘On the space of Type-2 interval with limit, continuity and differentiability of Type-2 interval-valued functions.’ (2019), arXiv preprint arXiv:1907.00644.
X. Rahman, M. S., Duary, A., Shaikh, A. A., & Bhunia, A. K., : ‘An application of parametric approach for interval differential equation in inventory model for deteriorating items with selling-price-dependent demand.’ Neural Computing and Applications, 32, (2020), 14069-14085.
XI. Rahman, M. S., Shaikh, A. A., & Bhunia, A. K., : ‘On Type-2 interval with interval mathematics and order relations: its applications in inventory control.’ International Journal of Systems Science: Operations & Logistics, 8(3), (2021), 283-295. 10.1080/23302674.2020.1754499
XII. Rahman, M. S., & Khatun, R., : ‘Generalised Arithmetic Mean-Geometric Mean Inequality And Its Application To Find The Optimal Policy Of The Classical EOQ Model Under Interval Uncertainty.’ Applied Mathematics E-Notes, 23, (2023), 90-99.
XIII. Stefanini, L., & Bede, B., : ‘Generalized Hukuhara differentiability of interval-valued functions and interval differential equations.’ Nonlinear Analysis: Theory, Methods & Applications, 71(3-4), (2009), 1311-1328. 10.1016/j.na.2008.12.005

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Abstract:

A verbal exchange today wishes for quick operational progress. This can be accomplished by replacing devices that are primarily concerned with commutation and logic with photon-based systems instead of the usual data service, the electron. The basic building blocks of superior frames are called gates. With the aid of these gates, various logical and mathematical operations can be performed. All-optical arithmetical and logical processes are eagerly expected in high-speed dialogue frameworks. In this chapter, we've introduced parallel models for adding two binary digits that are based on Sagnac gates with help from semiconductor optical amplifiers (SOA) and terahertz optical asymmetric demultiplexers (TOAD). We created a Full adder that works in parallel using only two TOADs as total switches. Using artificial neural networks (ANN), we have created a model of this circuit that is equivalent. Utilizing ANN, this circuit design has been validated. This optical circuit is now capable of synthesizing light as an input and successfully structuring the aspiration output in addition to speeding up calculation. This parallel circuit's biggest advantage is that it doesn't need synchronization for distinct inputs. An ANN model was used to analyze this circuit's performance in detail.

Keywords:

artificial neural networks,optical logic,semiconductor optical amplifier,Terahertz optical asymmetric demultiplexer,

Refference:

I. A. Bhattacharyya, D. K. Gayen, and T. Chattopadhyay, : ‘Alternative All-optical Circuit of Binary to BCD Converter Using Terahertz Asymmetric Demultiplexer Based Interferometric Switch.’ in Proceedings of 1st International Conference on Computation and Communication Advancement (IC3A–2013).
II. A. Poustie, K. J. Blow, A. E. Kelly, and R. J. Manning, : ‘All-optical full-adder with bit differential delay.’ Optics Communications 168 (1-4), 89–93 (1999). 10.1016/S0030-4018(99)00348-X
III. A. Ryou, J. Whitehead, M. Zhelyeznyakov, P. Anderson, C. Keskin, M. Bajcsy, and A. Majumdar, : ‘Free-space optical neural network based on thermal atomic nonlinearity.’ Photonics Research 9 (4), B128–B134 (2021). 10.1364/PRJ.415964
IV. A. Yariv and P. Yeh, : ‘Photonics: Optical Electronics in Modern Communications.’ Oxford University Press, UK, 6th Edition (2007).
V. B. Wang, V. Baby, W. Tong, L. Xu, M. Friedman, R. Runser, I. Glesk, and P. Prucnal, : ‘A novel fast optical switch based on two cascaded terahertz optical asymmetric demultiplexers (TOAD).’ Optics Express 10(1), 15–23 (2002). 10.1364/OE.10.000015
VI. D. K. Gayen, J. N. Roy, C. Taraphdar, and R. K. Pal, : ‘All-optical reconfigurable logic operations with the help of terahertz optical asymmetric demultiplexer.’ International Journal for Light and Electron Optics 122 (8), 711–718 (2011). 10.1016/j.ijleo.2010.04.024
VII. D. K. Gayen, T. Chattopadhyay, M. K. Das, J. N. Roy, and R. K. Pal, : ‘All-optical binary to gray code and gray to binary code conversion scheme with the help of semiconductor optical amplifier -assisted sagnac switch.’ IET Circuits, Devices & Systems 5 (2), 123–131 (2011). 10.1049/iet-cds.2010.0069
VIII. D. K. Gayen, : Optical arithmetic operation using optical demultiplexer. Circuits and Systems.’ Scientific Research, 7(11), 3485–3493 (2016). 10.4236/cs.2016.711296
IX. D. K. Gayen, ‘All-Optical 3:8 Decoder with the Help of Terahertz Optical Asymmetric Demultiplexer.’ Optics and Photonics Journal, 6 (7), 184–192, July (2016). 10.4236/opj.2016.67020
X. D. K. Gayen, : ‘Optical parallel half adder using semiconductor optical amplifier-assisted Sagnac gates.’ Journal of Mechanics of Continua and Mathematical Sciences, 17 (4), 1-7, April (2022). 10.26782/jmcms.2022.04.00001
XI. H. L. Minh, Z. Ghassemlooy, and W. P. Ng, “ ‘Characterization and performance analysis of a TOAD switch employing a dual control pulse scheme in high speed OTDM demultiplexer.’ IEEE Communications Letters 12 (4), 316–318 (2008). 10.1109/LCOMM.2008.061299
XII. J. H. Kim, S. H. Kim, C. W. Son, S. H. Ok, S. J. Kim, J. W. Choi, Y. T. Byun, Y. M. Jhon, S. Lee, D. H. Woo, and S. H. Kim, : ‘Realization of all-optical full-adder using cross-gain modulation.’ in Proceedings of the Conference on Semiconductor Lasers and Applications, SPIE 5628, 333–340 (2005). 10.1117/12.576410
XIII. J. Zhou, B. Huang, Z. Yan and J-C. G. Bünzli, Emerging role of machine learning in light-matter interaction. Light Science & Application 8, 84 (2019). 10.1038/s41377-019-0192-4
XIV. J. P. Sokoloff, P. R. Prucnal, I. Glesk, and M. Kane, : ‘A terahertz optical asymmetric demultiplexer (TOAD).’ IEEE Photonics Technology Letters 5 (7), 787–790 (1993). 10.1109/68.229807
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XVI. K. E. Zoiros, J. Vardakas, T. Houbavlis, and M. Moyssidis, : ‘Investigation of SOA-assisted Sagnac recirculating shift register switching characteristics.’ International Journal for Light and Electron Optics 116 (11), 527–541 (2005). 10.1016/j.ijleo.2005.03.005
XVII. K. E. Zoiros, P. Avramidis, and C. S. Koukourlis, : ‘Performance investigation of semiconductor optical amplifier based ultra-fast nonlinear interferometer in nontrivial switching mode.’ Optical Engineering 47 (11), 115006–11 (2008). 10.1117/1.3028348
XVIII. K. Mukherjee, : ‘Method of implementation of frequency encoded all-optical half- adder, half-subtractor, and full-adder based on semiconductor optical amplifiers and add drop multiplexers.’ International Journal for Light and Electron Optics. 122 (13), 1188–1194 (2011). 10.1016/j.ijleo.2010.07.026
XIX. M Suzuki, H. Uenohara, : ‘Invesigation of all-optical error detection circuitusing SOA-MZI based XOR gates at 10 Gbit/s.’ Electron. Lett, 45 (4), 224–225 (2009). 10.1049/el:20093461
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XXI. P. Ghosh, D. Kumbhakar, A. K. Mukherjee, and K. Mukherjee, : ‘An all-optical method of implementing a wavelength encoded simultaneous binary full-adder-full-subtractor unit exploiting nonlinear polarization rotation in semiconductor optical amplifier.’ International Journal for Light and Electron Optics 122 (19), 1757–1763 (2011). 10.1016/j.ijleo.2010.10.039
XXII. Q. Wang, G. Zhu, H. Chen, J. Jaques, J. Leuthold, A. B. Piccirilli, and N. K. Dutta, : ‘Study of all-optical XOR using Mach-Zehnder interferometer and differential scheme.’ IEEE Journal of Quantum Electronics 40 (6), 703–710 (2004). 10.1109/JQE.2004.828261
XXIII. S. Mukhopadhyay and B. Chakraborty, : ‘A method of developing optical half- and full-adders using optical phase encoding technique.’ in Proceedings of the Conference on Communications, Photonics, and Exhibition (ACP), TuX6, 1–2 (2009).
XXIV. T. Wang, S.-Y. Ma, L. G. Wright, T. Onodera, B. C. Richard and P. L. McMahon, : ‘An optical neural network using less than 1 photon per multiplication.’ Nature Communications 13 (123), 1–8 (2022).
XXV. X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, : ‘All-optical machine learning using diffractive deep neural networks.’ Science, 361 (6406), 1004–1008 (2018). 10.1126/science.aat8084
XXVI. X. Wu, J. A. Jargon, L. Paraschis and A. E. Willner, : ‘ANN-Based Optical Performance Monitoring of QPSK Signals Using Parameters Derived From Balanced-Detected Asynchronous Diagrams.’ IEEE Photonics Technology Letters 23 (4), 248–250 (2011). 10.1109/LPT.2010.2098025
XXVII. W. Gao, L. Lu, L. Zhou, and J. Chen, : ‘Automatic calibration of silicon ring-based optical switch powered by machine learning.’ Opt. Express 28 (7), 10438–10455 (2020). 10.1364/OE.388931
XVIII. Z. Yu, X. Zhao, S. Yang, H. Chen and M. Chen, : ‘Binarized Coherent Optical Receiver Based on Opto-Electronic Neural Network.’ IEEE Journal of Selected Topics in Quantum Electronics 26 (1), 1–9 (2020). 10.1109/JSTQE.2019.2931251

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