1 GOSPODARKA SUROWCAMI MINERALNYMI Tom Zeszyt 4/2 ROMAN MAGDA*, TADEUSZ WO NY*, STANIS AW G ODZIK*, JAN JASIEWICZ* Data management for the mine production planning and design Introduction The restructuring of the Polish mining sector during the economic transformation period has influenced the mine production management significantly. The transition of the economy to market rules has connected technical and technological elements of the production management with their economical and financial counterparts more closely. All these elements are characterized by high uncertainty and risk in the case of underground mining. It is sometimes not possible for the accepted management plan to be implemented according to the assumptions made during planning and design phases. It is therefore important to memorize the events and features which characterize the real implementation of the adopted production plan and to compare these data with the assumptions. Past experiences, especially recent ones, can be used for technology, economics and finances. By collecting such information in databases we obtain a possibility to create stochastic characteristics of processes and to use them to evaluate the risk of implementing the adopted production tasks. At present, the development of computer systems enables to collect large amounts of data and information, which later can be used to determine probability distributions and stochastic models. These will describe possible future processes together with their parameters and factors. * Department of Economics and Management in Industry AGH University of Science and Technology, Kraków, Poland.
2 Databases in the mine production planning and design The mine production planning and design is very often done by departments of production planning in each mine. This production is very specific because it is permanently accompanied by uncertainty and risk which result from geological, mining, technological and financial-economical aspects. Geological and mining conditions of the mine production are unique, therefore, it is necessary to learn them all the time . This imposes on the production planning teams the obligation of systematic learning. In order to facilitate the learning process special tools are needed. The tools must be able to register facts and assist the analysis and the assessment which are necessary to derive the afterthought being the starting point of the feedback in the learning cycle. When monitoring the practical implementation of the planning and design task, we can encounter phenomena which could not be foreseen, difficulties which require adjustments or natural conditions which make us change our assumptions. The system of monitoring and collecting information about the implemented production process is of great significance. The system has been developed to be used by the production planning team in correcting the current solution or creating a new one. The restructuring of the Polish mining sector has influenced the need for the quantitative and qualitative changes in the mine production management in a specific way. The facilitation of the design process, which enables a fast assessment of multi-variant solutions also in terms of uncertainty and risk typical for the mining enterprise in market conditions, ought to have been stressed more. The development of the computer technology has created a broader field for the use of mathematical methods and models in the mine production management practice. New methods for the registration of a large data and information resource about the production process have been opened. This resource can be used not only for monitoring the production process but also for planning its future development. It is also possible to use the resource to apply new calculation procedures, which could not be implemented due to their complicated nature and the lack of sufficient data for statistical treatment. Further increase of effectiveness of the coal mining sector is necessary, therefore, mine production and planning teams should consider mastering the management of fixed assets and human resources in their future projects. The teams should also put more emphasis on uncertainty and risk which are inseparably connected with the random nature of the mine production. Since mining enterprises operate in market conditions, which bring an additional element of uncertainty and stresses the need for mastering the tools and techniques which will take into consideration a broad aspect of uncertainty and risk in planning future mine production. Stochastic models and methods, which, by nature, take into consideration a random character of the modelled process are of great significance here. The production process in an underground coal mine is characterized by the use of expensive equipment, high expenses on mine works and high remuneration costs. From the point of view of the effectiveness level of the production process, the specific nature of the process requires the proper use of the expensive production property in the existing mining-
3 121 -geological conditions as the first factor in the mining works planning (both investment and production). Other factors to be considered are staff qualifications and the possibility of risk assessment of the production tasks, which were adopted in both business and technical- -economical plans. These tasks should be matched with the situation in the coal market. 2. Characteristics of the mine production management assisting system An attempt has been made by The Department of Economics and Management in Industry to develop an integrated mine production management assisting system in underground coal mines [1, 2]. Basic elements of the system include: data warehouse covering the data and information which have been collected for some time, are being currently collected as mining works are progressing, about the attributes of the mine production; the attributes can be used in the management of future mine production, database of natural and geological-mining conditions of this part of the deposit, which will be mined later, system of modelling, simulation and optimizing multiple variation mining works designs, 3D visualization system of mining works in space and time together with the projection of the quantities and parameters. The data warehouse covers data and information which are currently being updated and collected to feed the system with the data and input information necessary for mine production modelling [3, 4]. Geological-mining data base about the parts of the deposit for future development should be the base for making a dynamic deposit model, which can be used to modelling excavation designs in a specific time period (multiple variation solutions can be applied here). The model should enable to make digital maps and structural models of the deposit, strata profiles, seam thickness maps, maps of qualitative parameters, etc. The decision assistance in the mine production management using the system can be divided into the following phases: 1) building a variant set of mining works using the geological-mining data about the parts of the deposit for future development, 2) building a network model of mining works based on PERT and-or GERT network models, 3) dynamic simulation of mining works, together with determining probability distributions of the adopted design assessment criteria, using e.g. the Monte Carlo method and GERTS technique, 4) single- or multi-criteria optimization taking into consideration aspects of uncertainty and risk, 5) accepting a specific solution by the decision-makers, 6) monitoring the implementation of the accepted solution systematic restocking of the data warehouse.
4 122 A variant set for mining works can be made using data and information about geological- -mining conditions, equipment, work organization, logistics systems and all the elements of the production process. The set should also take into consideration available equipment and staff potential. The base for generating such a set is a mutual combination of panel localizations in the mining area, geometrical parameters of panels and longwall faces, working directions in the bed, longwall driving direction, technical equipment and work organization. The variant set for mining works can be represented with network models, whereas for the stochastic simulation of mining works in space and time together with the related cost and revenue streams the Monte Carlo method may be used. The fact that mines operate in market conditions may impose certain limits on the output level. The latter should not exceed sales possibilities form the business and technical- -economical plans. Taxonomic methods can help to choose the solution which will be compatible with the assumptions in the plans. GERTS method will evaluate the probability of keeping the necessary conditions and it will estimate the risk of the suggested solutions. Optimization criteria can be either technical or economical and they can be formed in different ways, depending on the circumstances. For example, from the point of view of the use of the production fixed assets, the following factors can be adopted: maximization of the assets profitability, minimization of the factor of non-utilized production capacity, minimization of fixed superfluous costs, etc. For the assessment of solution variants in the long run basic criteria used in investment effectiveness calculation can be adopted, for example the maximization of the net present value (NPV) and/or maximization of the internal rate of return (IRR), whereas for the assessment of solution variants sales value may be used in the short run. Other criteria, such as output maximization or minimization of labour consumption, can be used, too. Multi-criteria optimization is also possible by adopting several criteria and attributing them suitable ranks. Depending on the needs expressed by decision makers (mine or coal company management), a suitable visualization of mining works in time and space should be worked out. The visualization system should include the projection of observed quantities and parameters. The system should be interactive and it should consider the possibilities of current interventions and a quick solution in case of any change of the parameters. As soon as the solution has been accepted by the decision makers, the system should enable current monitoring the implementation of the solution and the comparison of the quantities obtained with the ones adopted before. The conclusions drawn from this comparative analysis should be taken into consideration when mastering the system in order to obtain solutions as close to the reality as possible. 3. Case study the use of the data base for the simulation of longwall advance rates Table 1 shows characteristics of three different longwalls mined in three different mines at different times. Two of them were driven with roof falls and the third one with filling
5 mining voids with hydraulic fill. Consecutive rows show parameters describing geological conditions of the walls, their geometrical parameters and process parameters. As it can be seen from the table, the walls have similar geological conditions but their geometrical parameters and equipment are different. Also, the number of the production shifts varies. Tables 2, 3 and 4 show daily longwall advance rates of walls 1, 2 and 3, respectively. For longwall 1 the number of data is 150, for longwall 2 the number is 306 and for longwall In each case, the sets are numerous enough to be statistically treated. 123 Selected technological and geological parameters of longwalls No: 1, 2 i 3 Wybrane technologiczne i geologiczne parametry przodków œcianowych nr 1, 2 i 3 TABLE 1 TABELA 1 Specification Units Longwall No 1 Longwall No 2 Longwall No 3 Roof control caving caving backfilling Seam number/layer 501/upper 510/upper 510/bottom Seam thickness [m] 7.0 to to to 10.4 Layer thickness [m] Dip along the face [ ] Dip along the panel length [ ] Roof arenaceous shale and sandstone mudstone arenaceous shale Floor mudstone, coal mudstone arenaceous shale Coal compressive strength [MPa] to 41.1 Roof compressive strength [MPa] 37.1 to Floor compressive strength [MPa] Coal bulk density [Mg/m 3 ] Panel length [m] Longwall length [m] Type of shearer KSW-475-ZBP/ (2BPH)D KGS 560/2BP/05 KGS-324/B Type of longwall conveyor PSZ-750/3x65/200 Glinik 260/724/BP R-750 Type of mechanized support FAZOS 18/34-POz Hydrotech 15/36 POz Pioma Jankowice 19/32.8 Oz" (5 items), PUMAR 12/32 Number of shifts with production [sh/day] 3 4 2
6 124 Rate of advance [m/day] longwall No 1 Szybkoœæ ruchu [m/dzieñ] przodek œcianowy nr 1 TABLE 2 TABELA 2 Month Day IX X XI XII I II III IV
7 125 Rate of advance [m/day] longwall No 2 Szybkoœæ ruchu [m/dzieñ] przodek œcianowy nr 2 TABLE 3 TABELA 3 Month Day V VI VII VIII IX X XI XII I II III IV VII VIII IX
8 126 Rate of advance [m/day] longwall No 3 Szybkoœæ ruchu [m/dzieñ] przodek œcianowy nr 3 TABLE 4 TABELA 4 Month Day II III IV V VI VII VIII IX
9 Using the STATISTICA package and statistical tests, such as Kolmogorov-Smirnov s test or Chi-square test it is possible to check the compatibility of probability distributions with the set of data . Figures 1 3 show the results of testing statistical distributions of daily advances of walls 1, 2 and 3, respectively. Table 5 depicts descriptive statistics of the distributions. For daily 127 Fig. 1. Results of statistical distribution testing longwall No 1 Rys. 1. Wyniki prób rozk³adu statystycznego przodek œcianowy nr 1 Fig. 2. Results of statistical distribution testing longwall No 2 Rys. 2. Wyniki prób rozk³adu statystycznego przodek œcianowy nr 2
10 128 Fig. 3. Results of statistical distribution testing longwall No 3 Rys. 3. Wyniki prób rozk³adu statystycznego przodek œcianowy nr 3 Mean value, variance and standard deviation of longwall advances Wartoœæ œrednia, wariancja i odchylenie standardowe ruchu przodka œcianowego TABLE 5 TABELA 5 Variable Sample Mean Min Max Variation Std. Deviation Longwall No Longwall No Longwall No advances of walls 1 and 2 it was possible to suit the standard distribution. For wall 3 the attempts to suit both the standard distribution and other STATISTICA distributions were unsuccessful. In this case the triangular distribution can be assumed. After the probability distribution of the wall advance rate has been determined, a simulation of wall advances in these parts of the deposit, which are going to be mined and whose mining-geological conditions are comparable with the ones described, can be done with the help of the Monte Carlo method.
11 129 Conclusions A mining company is a specific organization, which is learning all the time, mainly because of changing mining-geological conditions, technological progress and risk which always accompanies mining production. The work of teams which prepare the production, invent new ideas, plans and designs for future mining becomes more and more important since there is a continuous flow of information which can not be foreseen in advance. It is impossible to collect and transform the enormous amount of information without the assistance of latest computer technology. Therefore, mine production planning teams when learning should make the full use of the potential that modern computer technique offers. Data bases informing about mining works done so far are of great significance for the feedback in learning processes. The information collected in data bases can help mine production planning teams to improve their work in future. Quick and efficient use of this information together with good work conditions and motivation can lead to a higher effectiveness of mine production planning teams. The study was financed from State budget as research project No 4 T12A REFERENCES  M a g d a R., 2004 Zastosowanie modelowania matematycznego i techniki komputerowej w projektowaniu robót górniczych w kopalni wêgla kamiennego. Gospodarka Surowcami Mineralnymi, PAN, t. 20, z. 3,  M a g d a R., 2006 Koncepcja zintegrowanego systemu wspomagania zarz¹dzania produkcj¹ w kopalni wêgla kamiennego. Lubelskie Centrum Marketingu Sp. z o.o., Lublin.  M a g d a R., G ³ o d z i k S., W o Ÿ n y T., 2007a Zasady tworzenia baz danych geologiczno-górniczych dla przodków œcianowych kopalñ wêgla kamiennego. Gospodarka Surowcami Mineralnymi, PAN, t. 23, z. 1,  M a g d a R., G ³ o d z i k S., W o Ÿ n y T., 2007b Zasady tworzenia baz danych dla potrzeb symulacji stochastycznej kosztów produkcji w polach œcianowych. Gospodarka Surowcami Mineralnymi, PAN, t. 23, z. 2,  M a g d a R., G ³ o d z i k S., W o Ÿ n y T., J a s i e w i c z J., 2007 Wspomaganie procesu uczenia siê w projektowaniu eksploatacji górniczej. Szko³a Ekonomiki i Zarz¹dzania w Górnictwie 2007,  STATISTICA PL dla Windows, 1997 StatSoft, Kraków. ZARZ DZANIE DANYMI W PLANOWANIU I PROJEKTOWANIU PRODUKCJI KOPALNI S³owa kluczowe Wydobycie wêgla kamiennego, geologiczne i górnicze bazy danych, przodki œcianowe Streszczenie Kluczowe aspekty zarz¹dzania produkcj¹ górnicz¹ obejmuj¹ planowanie i projektowanie, które s¹ bardzo czêsto prowadzone przez wydzia³y planowania produkcji w ka dej kopalni. Produkcja ta jest bardzo specyficzna,
12 130 poniewa jej nieodzownym elementem jest niepewnoœæ i ryzyko, które wynikaj¹ z aspektów geologii, górnictwa, technologii, finansów i ekonomii. Równolegle ze znajomoœci¹ technologii górniczych, osoby odpowiedzialne za projekt dzia³añ górniczych musz¹ znaæ wszystkie aspekty górnictwa podziemnego, czego czasem nie mo na wyraziæ iloœciowo. Warunki geologiczne i górnicze produkcji kopalni s¹ specyficzne, z tego wzglêdu konieczne jest ci¹g³e ich poznawanie. Narzuca to na zespo³y planowania produkcji obowi¹zek systematycznego zdobywania wiedzy. Dla u³atwienia procesu nauki potrzebne s¹ narzêdzia specjalne. Narzêdzia musz¹ byæ w stanie rejestrowaæ fakty i pomagaæ w analizie i ocenie, które s¹ konieczne dla wyci¹gania wniosków, które s¹ punktem wyjœcia dla uzyskania informacji zwrotnych w cyklu uczenia siê. Podczas monitorowania praktycznego wdra ania zadañ planowania i projektowania, mog¹ wyst¹piæ zjawiska, których nie mo na przewidzieæ, trudnoœci wymagaj¹ce korekty lub warunki naturalne, która zmieniaj¹ za³o enia. System monitorowania i pozyskiwania danych odnoœnie wdra anego procesu produkcji nabiera kluczowego znaczenia. System opracowano dla wykorzystania przez zespó³ planowania produkcji w poprawianiu bie ¹cego rozwi¹zania lub tworzeniu nowego. Tak zebrane informacje mo na przechowywaæ w dedykowanej bazie danych. Opracowanie pokrótce analizuje koncepcjê bazy danych dla projektowania i planowania produkcji górniczej. Taka baza danych obejmuj¹ informacje o poprzednich dzia³aniach górniczych, obejmuj¹ce dane geologiczne i górnicze, technologiczne i finansowe. Baza danych jest aktualizowana stosownie do tego, aby mo na by³o uwzglêdniæ postêpy w dzia³aniach górniczych. Szczególne znaczenie ma rachunkowoœæ kosztów w punktach, gdzie te koszty s¹ generowane (z wykorzystaniem rachunkowoœci kosztów wydzia³owych, systemów kontrolingu i innych zapisów finansowych i systemów dokumentacji). Baza danych mo e równie zawieraæ œwie e i stosownie przetworzone dane do wykorzystania w projekcie i planowaniu, na przyk³ad mo e zawieraæ statystykê opisow¹ i rozk³ady prawdopodobieñstwa parametrów stosowanych w stochastycznych modelach dzia³añ górniczych (takie jak: koszty, przemieszczanie urz¹dzeñ górniczych, postêpy w górnictwie, czas zatrzymania, naprawy i inne parametry losowe) jak równie korelacjê i funkcje regresji. G³ównym przeznaczeniem bazy danych jest zbieranie informacji wymaganych dla stochastycznego modelowania dzia³añ górniczych z uwzglêdnieniem ryzyka i niepewnoœci. DATA MANAGEMENT FOR THE MINE PRODUCTION PLANNING AND DESIGN Key words Hard coal mining, geological and mining data bases, longwalls Abstract Key aspects of mining production management involve planning and design, which are very often done by departments of production planning in each mine. This production is very specific because it is inherently accompanied by uncertainty and risk which result from geological, mining, technological and financial- -economical aspects. Alongside the knowledge of mining technologies, those responsible for design of mining operations have to be aquatinted with all aspects of underground mining, which sometimes can not be expressed in quantitative terms. Geological and mining conditions of the mine production are unique, therefore, it is necessary to learn them all the time. This imposes on the production planning teams the obligation of systematic learning. In order to facilitate the learning process special tools are needed. The tools must be able to register facts and assist the analysis and the assessment which are necessary to derive the afterthought being the starting point of the feedback in the learning cycle. When monitoring the practical implementation of the planning and design task, we can encounter phenomena which could not be foreseen, difficulties which require adjustments or natural conditions which make change our assumptions. The system of monitoring and data acquisition about the implemented production process becomes of key importance. The system has been developed to be used by the production planning team in correcting the current solution or creating a new one. Thus collected information can be stored in the dedicated database.
13 131 The paper briefly reviews the concept of a database for the purpose of design and planning of the mining production. This database shall comprise the information on previous mining activities, covering the geological and mining data, technological and financial data. The database is updated accordingly, to account for the progress of mining operations. Of particular importance is the cost accounting at the points where these costs are generated (using the departmental cost accounting, controlling systems and other financial recording and documentation systems). The database might also comprise the fresh and duly processed data to be utilised in the design and planning, for example, it might contain descriptive statistics and probability distributions of parameters used in stochastic models of mining activities (such as: costs, advance of mining equipment, mining advance, shutdown time, repairs, and other random parameters) as well as correlation and regression functions. The main purpose of the database is to collect information required for stochastic modelling of mining operations taking into account the involved risk and uncertainty.