Computer-Aided Cut-off Grade Optimisation for Open Pit Mines

A mine planning team is tasked among other duties with designing a feasible mine plan which in turn maps out the daily running of the mining project. A mine plan revolves around a cut-off grade which is thoughtfully and uniquely selected while considering various aspects such as grade tonnage distribution, economic and operational parameters specific to a mine. Selection of a cut-off grade can be a daunting task often involving iterative and lengthy mathematical formulas which take huge amounts of time to execute, often leaving room for error. In the occurrence of such errors, a mining project can be faced with sequential outcomes that could even lead to premature closure. The cut-off grade is therefore a strategic variable that determines the economic viability of a mine, and hence return on investment. It is critical that the cut-off grade is optimal so as to maximise the net present value. Lane’s approach is a model that utilises several steps to yield one cut-off grade value. This algorithm is flexible and can be adjusted to include other factors specific to a mine. Regrettably, many mining companies continue to operate using inaccurate cut-off grades wrongly calculated or assumed. This has continuously led to frustrations due to losses and prematurely abandoned mines. This study focused on the development and implementation of an easy to use computer application based on Lane’s approach that runs on Windows platform, and hence targeting a larger user base for choosing an optimum cut-off grade for open pit mines. Keywords: Cut-Off Optimisation, Cut-Off Optimiser, Optimum Cut-Off Grades, Whittl

Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction

Abstract Backpropagation Neural Network (BPNN) is an artificial intelligence technique that has seen several applications in many fields of science and engineering. It is well-known that, the critical task in developing an effective and accurate BPNN model depends on an appropriate training algorithm, transfer function, number of hidden layers and number of hidden neurons. Despite the numerous contributing factors for the development of a BPNN model, training algorithm is key in achieving optimum BPNN model performance. This study is focused on evaluating and comparing the performance of 13 training algorithms in BPNN for the prediction of blast-induced ground vibration. The training algorithms considered include: Levenberg-Marquardt, Bayesian Regularisation, Broyden–Fletcher–Goldfarb–Shanno (BFGS) Quasi-Newton, Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Fletcher-Powell Conjugate Gradient, Polak-Ribiére Conjugate Gradient, One Step Secant, Gradient Descent with Adaptive Learning Rate, Gradient Descent with Momentum, Gradient Descent, and Gradient Descent with Momentum and Adaptive Learning Rate. Using ranking values for the performance indicators of Mean Squared Error (MSE), correlation coefficient (R), number of training epoch (iteration) and the duration for convergence, the performance of the various training algorithms used to build the BPNN models were evaluated. The obtained overall ranking results showed that the BFGS Quasi-Newton algorithm outperformed the other training algorithms even though the Levenberg Marquardt algorithm was found to have the best computational speed and utilised the smallest number of epochs.   Keywords: Artificial Intelligence, Blast-induced Ground Vibration, Backpropagation Training Algorithm

Optimising Shovel-Truck Fuel Consumption using Stochastic Simulation

Stochastic simulation was conducted to analyse the fuel consumption of a shovel-truck system. An example shovel-truck system, comprising a single shovel and four trucks was considered. At 95% confidence interval, the monthly simulated fuel consumption by the shovel-truck system was found to be about 198 127 litres against the actual fuel consumption of 203 772 litres, registering a variance of -2.70%. About 22 000 litres of fuel was consumed per month due to truck waiting. Optimising the fuel consumption and truck waiting time can result in significant fuel savings. The paper demonstrates that stochastic simulation is an effective tool for optimising the utilisation of fossil-based fuels in mining and related industries. Keywords: Stochastic, Simulation Modelling, Mining, Optimisation, Shovel-Truck Material Handlin

Agent-Based Optimization for Truck Dispatching in Open-Pit Mines

The mining industry has long recognized the value of dispatch systems in open pit mines as they reduce load and haul costs. Over the years, researchers have proposed many dispatch systems with various limitations and advantages. The simplest dispatch algorithms are the so called 1-truck-for-N-shovels dispatch strategy. These algorithms are limited by the fact that their objective functions do not consider all the objectives of a mine and cannot be applied to all possible truck-shovel configurations. They are also myopic in nature. However, they are simple and computationally efficient and do not require occasional updates of the upper stage problem as required in multi-stage dispatch algorithms. In this work, an agent-based truck dispatch algorithm that conceptualizes trucks as intelligent agents that make autonomous dispatching decisions to maximize their utility is proposed. The advantages of this algorithm includes utility functions that encapsulate all of management\u27s objectives and agent\u27s with broad situational awareness. They are also more suitable for autonomous trucks. We evaluate the new algorithm against a simple 1-truck-for-N-shovels dispatch strategies using discrete event simulation. The simulation results show that the new utility function has significant advantages over 1-truck-for-N-shovels inspired utility functions. Future work will incorporate adaptive behavior into the model via reinforcement learning algorithm

Discovering selective diguanylate cyclase inhibitors: From PleD to discrimination of the active site of cyclic-di-GMP phosphodiesterases

One of the most important signals involved in controlling biofilm formation is represented by the intracellular second messenger 3',5'-cyclic diguanylic acid (c-di-GMP). Since the pathways involved in c-di-GMP biosynthesis and breakdown are found only in bacteria, targeting c-di-GMP metabolism represents an attractive strategy for the development of biofilm-disrupting drugs. Here, we present the workflow required to perform a structure-based design of inhibitors of diguanylate cyclases, the enzymes responsible for c-di-GMP biosynthesis. Downstream of the virtual screening process, detailed in the first part of the chapter, we report the step-by-step protocols required to test the positive hits in vitro and to validate their selectivity, thus minimizing possible off-target effects