Afinidades y divergencias entre Sebilla y Sibilla

System dynamics theory, modelling, and the development of a decision making tool has been applied in the field of mining for a long time. Focusing on mining and coal mining fields, some researchers have used this conceptual theory to solve their problems in the mining fields. This is discussed in this section:

It began with Budzik, et.al., (1976) [61] developing an energy model, called FOSSIL1, by using the system dynamics theory. The purpose of the development was to understand energy balancing, to manage the USA reserves of coal, oil, and gas. Later, model updates of FOSSIL2 and FOSSIL3, etc., were published.

C. Roumpos, et.al., (2004) proposed the development of a decision making model for lignite deposit exploitability. The model was developed in the form of mathematical equations modelling, which included parameters in four sub models, (1) the deposit condition and the mine characteristics, (2) environmental and socioeconomic parameters, (3) competition, and (4) market [7]. The conceptual model of C. Roumpos, et.al., is shown in Figure 2.2.

The C. Roumpos mathematical model result of annual cash flow (Ai) in € is shown in equation (2.3) [7]: ‹ൌ ቈ ȽൈȽ ͳͲͲͲൈ൫’ȽǦ’Ƚ൯Ǧ ͲǤͺ͸ൈȽȽሺ…ˆ൅…‡Ƚሻ ȽȽ ቉ ൈͳͲ͸_{ሺͳǦ–ሻ൅–}Ƚ  (2.3)

Where, Pα = Capacity of the power plant (MW), Tα = Operating hour of the power plant (h/y),

Iα = Investment cost for power plant construction (€), Hα = Calorific Value (kcal/m3),

cpα = Production cost in power plant (€/kWh), ceα = Environmental cost (€/t), cf = Fuel

cost (€/m3_{), k = Construction time (y), N = Depreciation time or project life time (y),}

pα = Selling price of electricity (€/kWh), n = Power plant efficiency (%), ε = Discount

rate (%), and t = Tax rate (%).

Fan, et al. (2007) developed a system dynamics base model for coal investment in China. In this paper, a system dynamics model was developed taking the investment in the coal industry, available reserves, mine construction and coal supply capability into account [62].

Figure 2.3: Fan’s flow diagram of coal production and supply [62] PCsm NPCsm SPCsm Cmc MERS ARS ICGP GPI NARS MRS ERsm MERsm Psm CD ERtv MERtv Ptv Ism ICmw ERS

where:

ARS Available reserves for constructing mines MERsm Mining-employed reserves by stale-owned mines CD Coal demand MRS Mining reserves

Cmc Coefficient of mine construction MERtv Mining-employed reserves by town or village mines ERS Explored reserves NARS New available reserves for mine construction ERsm Extraction in state-owned mines NPCsm New production capacity of state-owned mines ERtv Extraction in town or village owned mines PCcm Production capacity of constructing mines GPI Geological prospecting investment PCnsp Production capacity of newly started project ICGP Investment coefficient in geological prospecting PCsm Production capacity of state-owned mines ICmw Investment coefficient of mining and washing of coal Psm Production of state-owned mine

Ism Investment in state-owned mine construction Ptv Production of town or village owned mines MERS Mining-employed reserves SPCsm Scrapped production capacity of state-owned mines

The results of Fan’s research showed many scenarios. The simulation of the model helped to find the economic scenario, where the available reserves would approximately reach 8.6 Bt/y, to meet the requirements of China’s expectation.

Caselles-Moncho, et al. (2006) studied the dynamic simulation model of a coal thermo- electric plant with a flue gas desulphurization system (FGD). This research developed a dynamic simulation model that had been used to present the likely responses of the electricity industries’ latest perturbations such as changes in environmental regulations, international fuel market evolution, restriction on fuel supply and increase on fuel prices, liberalisation of the European Electricity Market, and the results of applying energy policies and official tools such as taxes and emission allowances [63].

The results of Caselles-Moncho’s research showed the optimal strategy, including: (a) minimum energy production (b) specific net consumption of 2,207,000 t/GWh (the consumption curve means), (c) theoretical participation of the different fuels, (d) desulphurization running at 100% and (e) minimum commercialization of ashes, scoria, and gypsum [63].

O’Regan et al. (2001) from Ireland, published a paper about an insight into the system dynamics method: a case study in the dynamics of international minerals investment. This research presented an explanation of the system dynamics method. The aim of the model was to examine how environmental policy affects the investment and development decisions of the mining industry within the broader context of government minerals policy [64].

Figure 2.5: Principle of the O’Regan model diagram [64]

In summary, the O’Regan’s model aimed to encapsulate best practice in the field of system dynamics. It emphasised the difference between actual and perceived conditions

Investment in Exploration Discoveries Mining Activity Economics Viability of Marginal Deposits Clean Technology Investment in R&D Enviromental Legislation Public Satisfaction S S S S O S S S O O O R1 B1 B2 R2

as a basis for action. It made explicit the underlying assumptions as a basis for further expansion. It highlighted system structure as a catalyst for change. It did not by itself provide objective answers. Instead, it was a learning device and an aid to understanding. It was not a replacement for analytical thinking, but rather complementary to it [64].

Therefore, the decision support system of coal mine planning by using system dynamics model is a new and efficient tool to support making a decision on complex variables of new coal mining project. It can be fulfilled objectives of this thesis, which included the additional cost of social and environmental protection cost and mine closure cost.

The system dynamics modelling is the most suitable methodology in this purposed because it can deal with:

ƒ complex relationship of variables,

ƒ flexibility of changing value of input variables,

ƒ fast and no limit of calculation in a long period of mining project, and

ƒ easy to find sensitivity of variables and optimum solutions.

After reviewed and analysed, Vensim DSS software is chosen to develop the model because it has a free version for beginner to learn and the commercial version covers all functions that need to use in this thesis. Finally, it is also be the cheapest one of the popular software in this field.