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Table 1. Summary of major oxides and MgO descriptive statistics.

Table 1 Summary of major oxides and MgO descriptive statistics.

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Fig. | Framework for the prediction of the best and worst scenario. modeled by specifying the most likely probability distribution for the decision variables. In the third step, the model is simulated with hundreds or thousands of iteration (combination) of input variables. These combinations are randomly selected from _ the predefined distribution of input variables. These simulations result in potential outcomes and _ their distributions are obtained. From the simulation results and obtained distributions in the third step, the quarry manager may able to choose the best course of action. Therefore, simulation results also provide an effective understanding related to resource allocation.
Fig. | Framework for the prediction of the best and worst scenario. modeled by specifying the most likely probability distribution for the decision variables. In the third step, the model is simulated with hundreds or thousands of iteration (combination) of input variables. These combinations are randomly selected from _ the predefined distribution of input variables. These simulations result in potential outcomes and _ their distributions are obtained. From the simulation results and obtained distributions in the third step, the quarry manager may able to choose the best course of action. Therefore, simulation results also provide an effective understanding related to resource allocation.
Fig. 2 Probability distributions of major oxides and MgO of limestone dust. Based on the goodness of fit test, distributions followed by major oxides are SiO» (largest extreme value), CaO (smallest extreme value), Fe203 (lognormal), Al2O3 (Weibull), K2O (Weibull) and MgO (Weibull) (Fig. 4).
Fig. 2 Probability distributions of major oxides and MgO of limestone dust. Based on the goodness of fit test, distributions followed by major oxides are SiO» (largest extreme value), CaO (smallest extreme value), Fe203 (lognormal), Al2O3 (Weibull), K2O (Weibull) and MgO (Weibull) (Fig. 4).
Table 2. Percentage and probability of predictive values in the range.
Table 2. Percentage and probability of predictive values in the range.
Fig. 3 Frequency distribution of major oxides and MgO of shale.
Fig. 3 Frequency distribution of major oxides and MgO of shale.
Table 3. Purchased cost and required quantity limits of the raw material. The variation in the purchasing cost of limestone dust is due to variation in transportation cost because the raw material is transported from various locations. Manual planning involves a trial and error approach which is impractical in this case study and also may be time-consuming. Linear programming (LP) model indices are used to achieve optimum blending. Excel solver was used to developing an LP model. Linear programming-based optimization ensures the required quality and quantity of raw material for blending at minimum cost. Therefore, two optimized scenarios are presented out of 500 scenarios to evaluate the applicability of the proposed approach. The purchasing
Table 3. Purchased cost and required quantity limits of the raw material. The variation in the purchasing cost of limestone dust is due to variation in transportation cost because the raw material is transported from various locations. Manual planning involves a trial and error approach which is impractical in this case study and also may be time-consuming. Linear programming (LP) model indices are used to achieve optimum blending. Excel solver was used to developing an LP model. Linear programming-based optimization ensures the required quality and quantity of raw material for blending at minimum cost. Therefore, two optimized scenarios are presented out of 500 scenarios to evaluate the applicability of the proposed approach. The purchasing
preceding to raw mix design using Monte Carlo simulation for scenario analysis, can help in the decision about production planning.
preceding to raw mix design using Monte Carlo simulation for scenario analysis, can help in the decision about production planning.
Table 5. Quality parameters of raw material blending in scenario-I and II.
Table 5. Quality parameters of raw material blending in scenario-I and II.
Scenario analysis revealed that scenario-I and scenario-II provide 19150 tons and 18961 tons of raw material to fulfill the stockpile requirement. The optimum values of the indices of both scenarios are given in Table 5. Therefore, the optimum planning ensures the maximum use of limestone dust to avoid the maximum additives purchased from the market. Table 4. Raw material and their optimum cost for scenario-I and IL.
Scenario analysis revealed that scenario-I and scenario-II provide 19150 tons and 18961 tons of raw material to fulfill the stockpile requirement. The optimum values of the indices of both scenarios are given in Table 5. Therefore, the optimum planning ensures the maximum use of limestone dust to avoid the maximum additives purchased from the market. Table 4. Raw material and their optimum cost for scenario-I and IL.
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Production PlanningScenario AnalysisCement Factory
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