Application of Discrete Event Simulation in Mine Production Forecast

Mine production forecast is pertinent to mining as it serves production goals for a production period. Perseus Mining Ghana Limited (PMGL), Ayanfuri, deterministically forecasts mine production which sometimes result in significant variation from the actual production. This paper developed an innovative stochastic discrete event simulation model to predict production for two excavators at a pit in PMGL site using Arena® Software. Time and motion studies of the shovel-truck system were conducted to build the stochastic model and production was predicted for four weeks. The results showed a total average production of 210 414.86 BCM ± 3 301.59 BCM at 95% confidence interval. The total average production reflected a variance of 2.34% from the actual production of 215 341 BCM. The deviation was low as compared to the deterministic planned production variance which was 5.44%. Keywords: Stochastic, Simulation, Deterministic, Production Forecas

Case Study: Diesel Excavators Compared to Hybrid Excavators

The construction industry is constantly evolving and developing equipment that is more efficient, materials that promote sustainability and practices that emphasize lean construction principles to enhance productivity on projects. Modern day construction projects have become highly reliant on these ideals due to the highly competitive nature of the industry. In regards to heavy civil construction, specifically the underground utilities market, equipment costs count for a large percentage of overall project costs. Reducing these costs allows a contractor to be more competitive. As the focus on infrastructure construction continues to grow and expand, many heavy civil contractors are turning to alternative fuel option for heavy equipment to reduce operating costs and emissions. As a result, many heavy equipment manufacturers have invested into the production of hybrid heavy equipment for these purposes. Conversions from conventional diesel equipment to hybrid equipment have proven savings in operating costs, but for some contractors the more expensive ownership costs do not result in a more efficient machine

Transplantation of an alpine Carex-fen – a mitigation measure related to the construction of a reservoir in the Austrian Alps

Translocations are applied in the context of infrastructure projects to preserve certain vegetation types. Within the EIA of a large hydropower project in the Austrian Alps, manifold mitigation measures were defined. Among those, the transplantation of about 1.4 ha Carex-fen at an altitude of about 2000 m was defined. One year before the start of the construction works in 2021, basic infrastructure (roads) was established and different ecological measures were undertaken, e.g. translocation of amphibians to newly constructed habitats as well as the transplantation of the Carex-fen. The turf was cut from the initial area with an adjusted excavator shovel, delivered to a wheel loader which brought each single turf immediately to the target area, where another excavator mounted the turf in a pre-arranged area. At the donor site more than ½ of the area was based on wet gley, while especially areas in the vicinity of the river were based on fluvial gravel. With the 30 to 70 cm thick turfs also animals, e.g. Odonata, were transferred. Before the translocation a monitoring of the donor sites was carried out. The monitoring concept foresees a detailed monitoring of the newly established sites for 10 years. Herein we provide insights in the applied technology and summarize first results of the monitoring. Overall, our project is unique regarding the vegetation type, the technology, the size and the intensity of monitoring

Automated Measurement of Heavy Equipment Greenhouse Gas Emission: The case of Road/Bridge Construction and Maintenance

Road/bridge construction and maintenance projects are major contributors to greenhouse gas (GHG) emissions such as carbon dioxide (CO2), mainly due to extensive use of heavy-duty diesel construction equipment and large-scale earthworks and earthmoving operations. Heavy equipment is a costly resource and its underutilization could result in significant budget overruns. A practical way to cut emissions is to reduce the time equipment spends doing non-value-added activities and/or idling. Recent research into the monitoring of automated equipment using sensors and Internet-of-Things (IoT) frameworks have leveraged machine learning algorithms to predict the behavior of tracked entities. In this project, end-to-end deep learning models were developed that can learn to accurately classify the activities of construction equipment based on vibration patterns picked up by accelerometers attached to the equipment. Data was collected from two types of real-world construction equipment, both used extensively in road/bridge construction and maintenance projects: excavators and vibratory rollers. The validation accuracies of the developed models were tested of three different deep learning models: a baseline convolutional neural network (CNN); a hybrid convolutional and recurrent long shortterm memory neural network (LSTM); and a temporal convolutional network (TCN). Results indicated that the TCN model had the best performance, the LSTM model had the second-best performance, and the CNN model had the worst performance. The TCN model had over 83% validation accuracy in recognizing activities. Using deep learning methodologies can significantly increase emission estimation accuracy for heavy equipment and help decision-makers to reliably evaluate the environmental impact of heavy civil and infrastructure projects. Reducing the carbon footprint and fuel use of heavy equipment in road/bridge projects have direct and indirect impacts on health and the economy. Public infrastructure projects can leverage the proposed system to reduce the environmental cost of infrastructure project

Predicting fuel energy consumption during earthworks

This research contributes to the assessment of on-site fuel consumption and the resulting carbon dioxide emissions due to earthworks-related processes in residential building projects, prior to the start of the construction phase. Several studies have been carried out on this subject, and have demonstrated the considerable environmental impact of earthworks activities in terms of fuel consumption. However, no methods have been proposed to estimate on-site fuel consumption during the planning stage. This paper presents a quantitative method to predict fuel consumption before the construction phase. The calculations were based on information contained in construction project documents and the definition of equipment load factors. Load factors were characterized for the typical equipment that is used in earthworks in residential building projects (excavators, loaders and compactors), taking into considering the type of soil, the type of surface and the duration of use. We also analyzed transport fuel consumption, because of its high impact in terms of pollution. The proposed method was then applied to a case study that illustrated its practical use and benefits. The predictive method can be used as an assessment tool for residential construction projects, to measure the environmental impact in terms of on-site fuel consumption. Consequently, it provides a significant basis for future methods to compare construction projects.Peer ReviewedPostprint (author's final draft

Secondary Recycling of Smelter Slags

The modern structure of ferrous metallurgy slags recovery has been shown. Growth of recycling amounts is connected with high-capacity stationary and mobile crushing and screening plants (both foreign- and domestic made). The issues of environment protection against dust emissions still remain unsolved. Keywords: smelter slags, recycling technologies, crushing and screening plants, extraction and cleaning of metal inclusion

Analysis of earth-moving systems using discrete-event simulation

AbstractDiscrete-event simulation has been widely used technique in analyzing construction operations for the past three decades due to its great effect on optimizing cost and productivity. In this paper we will present Arena as a tool for simulating earthwork operations, the advantage of Arena is its easiness and flexibility in simulating most kinds of models in different areas of construction. A case study will be presented, a model will be built and results obtained to reveal the mentioned objectives