Computational and Mathematical Modelling of Industrial Assets: Alternative Numerical Approach

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Published: 2022-02-22

Page: 107-121


Godspower C. Abanum *

Department of Mathematics/ Statistics, Ignatius Ajuru University of Education, Port Harcourt, Nigeria.

I. C. Eli

Department of Mathematics/ Statistics, Federal University Otuoke, Yenagoa, Nigeria.

*Author to whom correspondence should be addressed.


Abstract

In this paper, we considered the numerical method in predicting the biodiversity loss and gain of industrial assets due to the variation of the per asset growth rate for industry in dealing with normal agriculture  on biodiversity scenario. However, when the model parameter values  (the per asset growth rate for industry in dealing with normal agriculture) is decreased and increased, the industrial assets variable changes. By comparing the loss and gain pattern in these two interacting industrial data, we have finite instance of biodiversity due to the application of numerical method (ODE45). The novel result we have obtained in this study have not been seen elsewhere.

Keywords: ODE45, ecosystem, biodiversity, matlab, industrial assets


How to Cite

C. Abanum, G., & Eli, I. C. (2022). Computational and Mathematical Modelling of Industrial Assets: Alternative Numerical Approach. Asian Journal of Pure and Applied Mathematics, 4(1), 107–121. Retrieved from https://globalpresshub.com/index.php/AJPAM/article/view/1463

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References

Godspower CA, Charles O, Ekaka-a EN. Numerical simulation of biodiversity loss: Comparison of numerical methods. International Journal of mathematics trend and technology. 2020;66(3):53-64.

Eleki AG, Akpodee RE, Ekakaa EN. Differential effects of a scenario of a non-additive environmental perturbation on biodiversity of ecospheric assets: Kalabari kingdom. Ecosystem African publication and research international. 2019;1-15.

Eleki AG, Ekakaa EN. Computational modelling of ecospheric assets: Alternative numerical approach. Afrcian scholar publication and research international. 2019;15(2):105-115.

Agyemang I, Freedom HI. An environmental model for the interaction of industry with two competing agricultural resources. Mathematical and computer modelling, Elsevier. 2009;49:1618-1643.

Apedaile LP, Freedom HI, Schilizzi SG, Solomonovich M. Equilibria and dynamics in an economic predator-prey model of agriculture. Mathematical and Computer Modelling. 1994;19(11):1-15.

Solomonovich M, Freedman HI, Apedaile LP, Schilizzi SGM, Belostotski L. Stability and bifurcations in an environmental recovery model of economic agriculture-industry interactions, Natur. Resource Modeling. 1998;11:35-79.

Solomonovich M, Apedaile LP, Freedman HI, Gebremedihen AH, Belostotski SMG. Dynamical economic model of sustainable agriculture and the ecosphere, Appl. Math. Comput. 1998;84:221-246.

Solomonovich M, Apedaile LP, Freedman HI. Predictability and trapping under conditions of globalization of agricultural trade: An application of the CGS approach, Math. Comput. Model. 2001;33:495-516.

Agyemang I, Freedman HI, Macki JW. An ecospheric recovery model for agriculture industry interactions, Diff. Eqns. Dyn. Syst. 2007;15:185-208.

Won WK, Jianqiang L. The relationship between the agricultural and industrial sectors in chinese economic developmeny. Agricultural economic report. 1997;368.

Misre AK, Lata K. A mathematical model to achieve sustainable forest management. Industrial journal of modelling, simulation and scientific computing. 2015a;6(4):295-301.

Frosch RA, Gallopoulo NE. Strategies for manufacturing. Scientific American. 1989;261(3):144-152.

Frosch RA. Industrial ecology: Adapting technology for a sustainable world. Environment Sceience and policy for sustainable development. 1995;37(10):17-37.

Korhonen J. Two paths in industrial ecology: Applying the product-based and geographical approachs. Journal of environmental planning and management. 2002;45(1):39-57.

El-Hagger SM. Sustainable industrial design and waste management. Oxford, Elsevier Academic press; 2007.

Fabian Schuze. Classification and development of mathematical model and simulation for industrial ecology. University of Rhode Island; 2014.