Article Versions
Export Article
Cite this article
  • Normal Style
  • MLA Style
  • APA Style
  • Chicago Style
Research Article
Open Access Peer-reviewed

Sustainability Evaluation for Metallurgical Value Addition Process Facility

A. Ayyakkannu , P.C. Naganoor, P.S. Kumar, Basavaraj Hatti
Applied Ecology and Environmental Sciences. 2020, 8(6), 526-529. DOI: 10.12691/aees-8-6-27
Received September 25, 2020; Revised October 27, 2020; Accepted November 04, 2020

Abstract

The sustainability of raw materials manufacturing industries is represented by the inscribed triangle and inverse triangle models. The significance of the inverse triangle is to satisfy the uncertainty when the upward triangle is facing multiple crises. The sustainability effectiveness is depending on indexes, indices, and variables that are used for evaluation. The indexes reduce the dimensionality problem, indices balance the equilibrium sustainability and variables determine the process capability. Sustainability evaluation of metallurgical value addition processes indicates that the modeling possesses some level of sustainability without satisfying the profit index. The level of sustainability is ineffective and susceptible to incurring a loss when applied to semi-finished products manufacturing. Therefore, the aspect of sustainability is the profitability of the value addition processes. The effectiveness of the model will quantify the triangles and criteria for uncertainty satisfaction. Hence, a case study is undertaken for estimating the sustainability of the Port-based Pellet and Pig Iron project owned by KIOCL Ltd., Panambur, and Mangalore. The indexes based evaluation of the metallurgical value-added process facility helps to reduce the complexity of the problem and computing difficulties.

1. Introduction

Indian steel sectors are facing sustainability challenges. The iron and steel industries need huge raw materials to produce finished steel. Consumption of raw materials, fuels, and product demands are often alter the sustainability of the project or enterprise. A semi-finished product manufacturing facilities facing sustainability challenges due to the market value and demand fluctuations 1. Sustainability of development methodologies like a triangle and inverse triangle modeling 2, are in use for evaluation of the sustainability of a project or enterprise. Multiple approaches exist for sustainability evaluation and it varies from country to country and organization to organization 3. The triangle and inverse triangle model was implemented two decades ago for the metallurgical plants like combined merchant pellet and pig iron plants for sustainability evaluation. The value addition processes are manufacturing semi-finished products and these products are under the purview of the framework of the raw material. The problems of the project are the capacity underutilization and non - utilization of the created capital assets. The value addition processes involve higher expenses which deprive profitability; thereby the sustainability is becoming ineffective. Sustainability and profitability are the two sides of the same coin; still, it varies as the objectives are different 4. Sustainability is focusing on the conservations of resources 5 and profitability for the longevity of the capital assets 6. The cube and full arc as indexes are used to represent the value-added methods. Pareto principle and natural logarithm methods are used for sustainability evaluation. Risk is the indie to the sustainability of the enterprise 7. Sustainability management of a project requires risk and constraints as indices. The constraints and risks must be differently viewed in the metallurgical value addition. Constrains are dynamic variables and decide the profitability and risks are latent variables and decide sustainability. Both indices are used to find the aspects of sustainability. Indexes and indices help to reduce the computing difficulties of profitability, sustainability and also helping to optimize the modeling. The paper is aiming to quantify the values of sustainability indices and drawing inferences for sustainability aspects, which will assist the decision makers for implementation of national steel policy 8 and sustainable development goal 9.

2. Methods

Figure 1 shows the in a scripted triangle and inverse triangle modeling for the sustainability of the raw material industries 10. The reorientation of the modeling as part of quantifications. The upward triangle is representing the pelletizing and the downward triangle representing the pig iron process. The sustainable condition and achievability are shown in Figure 2. The modeling is facing profit index satisfaction crises ( MR/(P cv) < 1, where MR - market rate and PCV - cost values of the product). Another problem is non - utilization of the part of the capital assets. The scenarios are encouraged to find out the sustainability effects of the modeling.

Cube 11 and uncertainty arc length 12 as indexes are chosen to represent the variables. Figure 3 shows the cube index with input-output variables (resources, value addition methods, products). The economic variables (resources, value addition methods, standard loss) are shown in the Figure 4 in the full arc index.

3. Evaluation of sustainability indices

Cube index: Pareto 80 /20 and Pareto 64 /4 rules are used to evaluate sustainability 13 and expressed as 80 % of 80 = 64 % = sustainable level; 20% of 20 = 4% = constraints level. The balance value of 32 % is considered a risk. Pareto 80/20 rule is based on a 100% index. Therefore, the 100% index is expressed as 64 % (sustainability) + 4 % (constraints) + 32 % (risk) = 100 %.

Full arc index: Fractional arc lengths are devised based on the ratio of the isosceles right-angle triangle (1: 1: 1.41 = 3.41 units) and full arc length is equal to the sum of the ratios of the two triangles (3.41 + 3.41 = 6.82 units). The diameter of the full arc is equal to 2.170 units as path length. The risk arc length (ρ) is equal to 1.993 units, the arc length of resources (r) is equal to 1.993 units and value-addition methods arc 2.785 units. These arc lengths are derived based on the ratios of the triangle. As per the natural logarithm methodology, the constraints and risk are evaluated as follows.

Where; VAM is = r + m and

Hence, Sustainability is given as = 100-(41%+30) = 29%. Therefore, indices values are 41 % constraints, 30 % risk, 29 % sustainability and 2.170 unit path length. The comparisons of the indices values shown in the Figure 5.

The processing capability and sustainability of entities (project and enterprise) can be expressed by structural equation modeling 13 using input-output variables as follows, Project = E ( VAP) = r , m = ; P i = 1,2,3…, where E - enterprise by value addition processes; r- high-value resources like iron ore and coke, m - value addition methods, P i = 1,2,3 = Q P, PCV , where QP - the quantity of output as products; PCV - Cost value of the products and Pi = 1,2,3..number of products. The observed variables are r, m, P, which decides the constraints. The latent variables are QP and PCV, which decides the risk. Therefore, the constraints and risk will decide the indexes for process capability, which constitutes profits index as < 1, profitability index as > 1. The sustainability index is decided based on value creation as > 1, where PSV - sale value of the products. If these conditions are not satisfied, it is inferring that constraints and risks are violating the process capability.

4. Discussion

Figure 6, shows the type of path (non - linear path due to merchant characterizations of the value addition plants) after reorientation of the inscribed triangle and inverse triangle modeling. Figure 7 shows the sustainability indices values derived by the cube index. Figure 8 shows further reorientation of the triangle and inverse triangle as a square. Square is representing the sustainability of the project with sustainability indices values and path length.

5. Optimization of Modeling

It is assumed that triangle and inverse modeling as risk and input-output variables as constraints. How to improve profitability and sustainability is the process optimization task 14. Figure 9 shows improving the profitability by improving as per triangles marking and the ratio of triangles. Figure 10 shows sustainability maximization by path length (diameter of full arc), which can be calculated based on circumference. Figure 11 shows the conditions for distributed constraints and uncertainty satisfaction. The right angle triangle condition satisfaction by Pythagorean triple is the feature, which ensures sustainability and profitability. Integral power logarithm is used to devise maximization - minimization indices.

6. Conclusion

The inscribed triangle and inverse modeling is tested for sustainability effectiveness by cube and full arc indexes. Pareto principle and natural logarithm methods are used to evaluate the values of sustainability indices. Both methods can derive different values for the same indices. These values are inferring the aspects of sustainability. Higher risks reduce sustainability whereas constraints reduce profitability. Balancing of these indices is necessary for sustainability effectiveness. Indexes based evaluation helps to reduce the complexity of the problem and computing difficulties.

References

[1]  Snyder, L.V, “Facility location under uncertainty - A review”, 2005. www.lehigh.edu
In article      
 
[2]  Jesscia, E., Nikhil, T., Marc, A., “Measuring sustainable development in industrial minerals mining” International Journal of Mining and Mineral Engineering, 5(1). 2014.
In article      View Article
 
[3]  Welsch, “Constructing meaningful sustainability indices”, Applied research in environmental economics, 2014 www.semeanticscholar.org
In article      
 
[4]  Christopher, “Risk management and sustainability - Two sides of the same coin” University of Toronto, Ontario, Canada, 2017. https://www.forbes.com/risk-managment.
In article      
 
[5]  Hamza, Aliyu, G., “Abu Baker - A study of factors that support the longevity of business enterprises” 11(1). 53-59. 2018. www.iosrjournals.org/vol.20.
In article      
 
[6]  Greg, Johnson, “Asset optimization”, 5 lessons from 10 years in mining, 2005. cdn.iotwf.com/resource.
In article      
 
[7]  ISO 31000, “Enterprise risk management”, 2018. https://en.wikipedia.org/wiki.
In article      
 
[8]  NSP, “National Steel policy -2017”, Ministry of Steel, 2017. www.steel.gov.in
In article      
 
[9]  SDG, “Sustainable development goals”, 2015. en.wikipedia.org.
In article      
 
[10]  Elza, Bontempi, “Raw materials and sustainability indicators”, 2017. www.springer.com/in/book/ 9783319608303 /.
In article      View Article
 
[11]  Steven, R., Williams, Jean., M., Richardson, “Geometallurgical mapping; A new approach that reduces technical risk”, 2004. www.semantic scholar.org.
In article      
 
[12]  Wei, Liu., “Uncertain programming models for shortest path problem with uncertain arc length”, 2010. pffs.sememantic scholar.org.
In article      
 
[13]  Gareth, Kline., “Structural equation modeling”, 2011. https://en Wikipedia .org
In article      
 
[14]  Lordrina, M., “Productivity improvement through process optimization”, Case study of a plastic manufacturing and sales company, CICIE, 2014.
In article      
 

Published with license by Science and Education Publishing, Copyright © 2020 A. Ayyakkannu, P.C. Naganoor, P.S. Kumar and Basavaraj Hatti

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Cite this article:

Normal Style
A. Ayyakkannu, P.C. Naganoor, P.S. Kumar, Basavaraj Hatti. Sustainability Evaluation for Metallurgical Value Addition Process Facility. Applied Ecology and Environmental Sciences. Vol. 8, No. 6, 2020, pp 526-529. http://pubs.sciepub.com/aees/8/6/27
MLA Style
Ayyakkannu, A., et al. "Sustainability Evaluation for Metallurgical Value Addition Process Facility." Applied Ecology and Environmental Sciences 8.6 (2020): 526-529.
APA Style
Ayyakkannu, A. , Naganoor, P. , Kumar, P. , & Hatti, B. (2020). Sustainability Evaluation for Metallurgical Value Addition Process Facility. Applied Ecology and Environmental Sciences, 8(6), 526-529.
Chicago Style
Ayyakkannu, A., P.C. Naganoor, P.S. Kumar, and Basavaraj Hatti. "Sustainability Evaluation for Metallurgical Value Addition Process Facility." Applied Ecology and Environmental Sciences 8, no. 6 (2020): 526-529.
Share
[1]  Snyder, L.V, “Facility location under uncertainty - A review”, 2005. www.lehigh.edu
In article      
 
[2]  Jesscia, E., Nikhil, T., Marc, A., “Measuring sustainable development in industrial minerals mining” International Journal of Mining and Mineral Engineering, 5(1). 2014.
In article      View Article
 
[3]  Welsch, “Constructing meaningful sustainability indices”, Applied research in environmental economics, 2014 www.semeanticscholar.org
In article      
 
[4]  Christopher, “Risk management and sustainability - Two sides of the same coin” University of Toronto, Ontario, Canada, 2017. https://www.forbes.com/risk-managment.
In article      
 
[5]  Hamza, Aliyu, G., “Abu Baker - A study of factors that support the longevity of business enterprises” 11(1). 53-59. 2018. www.iosrjournals.org/vol.20.
In article      
 
[6]  Greg, Johnson, “Asset optimization”, 5 lessons from 10 years in mining, 2005. cdn.iotwf.com/resource.
In article      
 
[7]  ISO 31000, “Enterprise risk management”, 2018. https://en.wikipedia.org/wiki.
In article      
 
[8]  NSP, “National Steel policy -2017”, Ministry of Steel, 2017. www.steel.gov.in
In article      
 
[9]  SDG, “Sustainable development goals”, 2015. en.wikipedia.org.
In article      
 
[10]  Elza, Bontempi, “Raw materials and sustainability indicators”, 2017. www.springer.com/in/book/ 9783319608303 /.
In article      View Article
 
[11]  Steven, R., Williams, Jean., M., Richardson, “Geometallurgical mapping; A new approach that reduces technical risk”, 2004. www.semantic scholar.org.
In article      
 
[12]  Wei, Liu., “Uncertain programming models for shortest path problem with uncertain arc length”, 2010. pffs.sememantic scholar.org.
In article      
 
[13]  Gareth, Kline., “Structural equation modeling”, 2011. https://en Wikipedia .org
In article      
 
[14]  Lordrina, M., “Productivity improvement through process optimization”, Case study of a plastic manufacturing and sales company, CICIE, 2014.
In article