Data field for decision making in maintenance optimization: An opportunity for energy saving

Energy saving is an important issue for the industrial sectors which is a crucial factor in energy cost, waste reduction and environmental management. Maintenance operations are fundamental in granting machineries and processes energy saving, given the capability of optimising them thanks to a predictive model. The major challenge of maintenance optimization is to implement a maintenance strategy, which maximizes availability and efficiency of the equipment, controls the rate of equipment deterioration, ensures the safe and environmentally friendly operation, and minimizes the total cost of the operation which means the both production and energy cost. To ensure that the plant achieved the desired performance it needs a track performance on maintenance operations and maintenance results. In addition it needs the relationship between the inputs of the maintenance process and the outcomes in terms of total contribution to manufacturing performance and objectives. In addition to normal wear and deterioration, other failures may occur especially when the equipment are pushed beyond their design limits or due to operational errors. As a result, equipment downtime, quality problems, energy losses, safety hazards or environmental pollution become the obvious outcomes. All these outcomes have the potential to impact negatively the operating cost, profitability, customers' demand satisfaction, and productivity among other important performance requirements. The developed model in this study is an integration of a probabilistic and a deterministic model based on balanced cost and benefits, able to support decision making. This is described through the application to a case study: the optimisation of maintenance operations in energy consuming equipment in the production process of bituminous materials, based on the energy audit from the field and a part of the results are illustrated in this work