5 research outputs found
Determination of iron procurement strategy for manufacturing companies
The objective of this paper is to evaluate the price development of iron (steel rebar and hot rolled coil steel) on commodity exchanges, to determine the dependence of the price of iron on prices of other major commodities (crude oil and natural gas), to forecast its future development and to propose a particular iron procurement strategy for manufacturing companies in the South Bohemian Region until the end of 2028. The content analysis method was selected to evaluate the price development. It was also used to assess the dependence of iron prices on other major commodities, which was considered using the correlation analysis method. The artificial neural network method, multilayer perceptron networks, was selected and used to forecast future price development. All calculations are performed using Statistica software (version 13). Linear regression is conducted using different functions, with 1,000 neural structures being generated each time, out of which 5 structures showing the best characteristics are selected. These are retained to forecast future prices for the 2023-2028 period in three experiments. Results are presented in tables and graphs processed in Microsoft Excel. Based on the selected variants of future steel price forecasting, a specific iron procurement strategy can be recommended for manufacturing companies in the South Bohemian Region until the end of 2028
Forecasting methods in energy planning models
Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs
Decarbonization cost of Bangladesh's energy sector: Influence of corruption
As a rapidly developing lower-middle income country, Bangladesh has been maintaining
a steady growth of +5% in the gross domestic product (GDP) annually since
2004, eventually reaching 7.1% in 2016. The country is targeting to become uppermiddle-
income and developed by 2021 and 2041 respectively, which translates to an
annual GDP growth rate of 7.58% during this period. The bulk of this growth
is expected to come from the manufacturing sector, the significant shift towards
which started at the turn of this century. Energy intensity of manufacturing-based
growth is higher, the evidence of which can be seen in the 3.17 times increase in
national energy consumption between 2001 and 2014. Also, Bangladesh aims to
achieve 100% electrification rate by 2021 against an annual population growth rate
of 1.08%. With the increasing per capita income, there is now a growing middle
class fuelling the growth in demand for convenient forms of energy. Considering
the above drivers, the Bangladesh 2050 Pathways Model suggested 35 times higher
energy demand than that of 2010 by 2050. The government and private sector have
started a substantial amount of investments in the energy sector to meet the signi
ficant future demand. Approximately US250 billion in 2050 under HCS, which can be reduced 23% under
ZCS. The cost of decarbonization would be 3.6, 3.4 and 3.2 times under average
cost of MCS, LCS, and ZCS, than that of HCS. As the energy sector of Bangladesh
is under rapid development, the accumulated capital would be comparatively high
by 2050. However, fuel cost can be significantly reduced under LCS and ZCS which
would also ensure lower emissions. The study suggested that energy mix change,
technological maturity, corruption and demand reduction can influence the cost
of decarbonization. However, the most significant influencer for the decarbonization
of Bangladeshi energy sector would be the corruption. Results showed that if
Bangladesh can minimize the effect of corruption on the energy sector, it can reduce
the cost of decarbonization 45-77% by 2050 under MCS, LCS, and ZCS
Improving the sustainability of coal SC in both developed and developing countries by incorporating extended exergy accounting and different carbon reduction policies
In the age of Industry 4.0 and global warming, it is inevitable for decision-makers to change the way they view the coal supply chain (SC). In nature, energy is the currency, and nature is the source of energy for humankind. Coal is one of the most important sources of energy which provides much-needed electricity, as well as steel and cement production. This manuscript-based PhD thesis examines the coal SC network as well as the four carbon reduction strategies and plans to develop a comprehensive model for sustainable design. Thus, the Extended Exergy Accounting (EEA) method is incorporated into a coal SC under economic order quantity (EOQ) and economic production quantity (EPQs) in an uncertain environment. Using a real case study in coal SC in Iran, four carbon reduction policies such as carbon tax (Chapter 5), carbon trade (Chapter 6), carbon cap (Chapter 7), and carbon offset (Chapter 8) are examined. Additionally, all carbon policies are compared for sustainable performance of coal SCs in some developed and developing countries (the USA, China, India, Germany, Canada, Australia, etc.) with the world's most significant coal consumption. The objective function of the four optimization models under each carbon policy is to minimize the total exergy (in Joules as opposed to Dollars/Euros) of the coal SC in each country. The models have been solved using three recent metaheuristic algorithms, including Ant lion optimizer (ALO), Lion optimization algorithm (LOA), and Whale optimization algorithm (WOA), as well as three popular ones, such as Genetic algorithm (GA), Ant colony optimization (ACO), and Simulated annealing (SA), are suggested to determine a near-optimal solution to an exergy fuzzy nonlinear integer-programming (EFNIP). Moreover, the proposed metaheuristic algorithms are validated by using an exact method (by GAMS software) in small-size test problems. Finally, through a sensitivity analysis, this dissertation compares the effects of applying different percentages of exergy parameters (capital, labor, and environmental remediation) to coal SC models in each country. Using this approach, we can determine the best carbon reduction policy and exergy percentage that leads to the most sustainable performance (the lowest total exergy per Joule). The findings of this study may enhance the related research of sustainability assessment of SC as well as assist coal enterprises in making logical and measurable decisions