This paper presents the design of a multi-layer feedforward neural network-based model predictive controller (NNMPC) for a two degree-of-freedom (DOF), quarter-car servohydraulic vehicle suspension system. The nonlinear dynamics of the servo-hydraulic actuator is incorporated in the suspension model and thus a suspension travel controller is developed to indirectly improve the ride comfort and handling quality of the suspension system. A SISO feedforward multi-layer perceptron (MLP) neural network (NN) model is developed using input-output data sets obtained from the mathematical model simulation. Levenberg-Marquandt algorithm was employed in training the NN model. The NNMPC was used to predict the future responses that are optimized in a sub-loop of the plant for cost minimization. The proposed controller is compared with an optimally tuned constant-gain PID controller (based on Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road input disturbance. Simulation results demonstrate the superior performance of the NNMPC over the generic PID -based in adapting to the deterministic road disturbance.

Occupational noise-induced hearing loss (ONIHL) is classified as the leading work-related disability in the mining industry. ONIHL has a negative impact, on not only the health and occupational productivity of affected individuals, but arguably also on the country's mining industry and economy. Hearing conservation programmes (HCPs) are an effective strategy in the management of ONIHL. However, current literature indicates that HCPs are not achieving the anticipated and desired outcomes in the South African mining sector despite the efforts focused on the management of ONIHL. Therefore, the purpose of this study is to propose the use of a feedback-based noise monitoring model as a tool for monitoring and managing ONIHL in the South Africa's mining sector. This model is a basic static feedback model with practical applications such as estimating, monitoring. and providing quantitative information to aid miners, mining administrators, and policy-makers in decision-making around HCPs. Additionally, the model could form part of an early intervention and management strategy for ONIHL in the workplace. The strength of this model, although currently static, is that it encompasses all the pillars of HCPs and takes into account the policies concerned with the management of ONIHL in the mining sector.

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Results: Some of the case studies used have cited a reduction in the noise intensity emitted by machinery from a range of 93 dBA -104 dBA to a range of 90 dBA -94 dBA, demonstrating quite a significant reduction in the noise emission of the equipment. This article further provides recommendations on how South African mines could contextualise these methods. Conclusion:One of the key recommendations is encouraging the South African mining industry towards the documenting and publishing of those engineering noise control methodologies that have proven to be effective for shared best practice. A need was identified for extensive research to be conducted and documented evidence to be made available to assist the South African mining industry with locating and assessing current engineering controls available in South Africa. Machines and processes that require noise control should be identified and, lastly, the current barriers to the use of engineering noise control methodologies should be identified, with the main goals of finding ways to overcome the noise challenges in the mines.