International Journal of Mineral Processing and Extractive Metallurgy 2021; 6(3): 67-72 http://www.sciencepublishinggroup.com/j/ijmpem doi: 10.11648/j.ijmpem.20210603.14 ISSN: 2575-1840 (Print); ISSN: 2575-1859 (Online) Research/Technical Note Reducing Uncertainties in Gold Plant Design and Operations Charles Amoah 1, * , Grace Ofori-Sarpong 2 , Richard Kwasi Amankwah 2 1 Asanko Gold Ghana Limited, Obotan Operations, Manso Nkran, Ghana 2 Department of Minerals Engineering, University of Mines and Technology, Tarkwa, Ghana Email address: * Corresponding author To cite this article: Charles Amoah, Grace Ofori-Sarpong, Richard Kwasi Amankwah. Reducing Uncertainties in Gold Plant Design and Operations. International Journal of Mineral Processing and Extractive Metallurgy. Vol. 6, No. 3, 2021, pp. 67-72. doi: 10.11648/j.ijmpem.20210603.14 Received: September 1, 2021; Accepted: September 16, 2021; Published: September 27, 2021 Abstract: The conventional way of designing a plant is to determine the characteristics of rocks in terms of crushability, grindability and other properties that affect the mill throughput. These properties are most of the time determined from drill cores obtained during the exploration period. Such initial exploration campaigns drill to levels shallower than the real pit that will be developed. Thus, as mining pits become deeper, the ore characteristics change and begin to impact negatively on the expected mill throughput. Such situations necessitate modification of the plant, and the first intervention usually is to supplement the initial energy input with additional size reduction equipment to achieve the required throughput. However, reconsidering the inputs used in determining the initial plant selection would help in reducing the setbacks during the operational period. To help reduce uncertainties and develop a predictive tool, this study considered a greenfield drilled up to 273 m, and the core samples obtained were tested to ascertain the variations in Bond work index to depths beyond 500 m. The study showed that within the section of the Asankragwa belt investigated, Bond work indices increased from 10.3 kW/t at the surface to 16.5 kW/t at a depth of 273 m. The Bond work index was established as a function of vertical depth in a pit (x) with the relation BWI=6E-05x2 + 0.0071x + 9.8816. The predicted value at 280 m was 16.3 kW/t while that of the blend was 15.8 kW/t, giving an error of 4%. This novel relationship between the BWI and depth predicts the BWI beyond 500m with minimum mean square error. The use of the novel Bond work index and depth relationship will eliminate the uncertainty beyond the drilled depth and give a clear understanding of what the rock characteristics will be as pits become deeper. In addition, a savings of US$62,500 per diamond drill hole and US$25,000 per one reverse drilling after the 250 m depth can be made by the use of this model. This can result in massive savings considering the number of holes that would have to be drilled across the length of the pit. Keywords: Bond Work Index (BWI), Asankragwa Belt, Plant Operations, Uncertainties, All in Sustaining Cost (AISC) 1. Introduction According to Vallee [1, 2] investments in and the development of minerals resource projects depend on the quantity (tonnage) and quality (grades) of the resources in the deposit. However, due to variations in geological structures, different and complex genesis of ore bodies and other intangible mineral factors, the prediction of mineralisation is with great uncertainty. In the minerals industry, decision making is based primarily on data obtained from samples and sampling. This issue cuts across grass root exploration through to resource upgrade, resource or reserve estimation, production and mineral processing or extraction [3-6]. Challenges and errors in the prediction and evaluation of mineral resources, and hence geological forecasting occur due to uncertainties of the orebody, characteristics and geological processes, geological data and geological measurement inaccuracy. [7-13]. In the evaluation of mineral deposits, geological and grade uncertainties may be reduced by several techniques including probability theory, geostatistics, geological geometry, fuzzy sets/logic and neural networks [14-20]. Several codes and classifications are well established in grade and ore reserve estimation.