Abstract
Optimizing the multilayer thermal insulation of pipelines transporting liquids and gases at higher than ambient temperatures is crucial for heat energy conservation and cost optimization. This study utilizes a multi-objective genetic algorithm to optimize the multilayer thermal insulation thickness around a pipe carrying fluid to minimize heat loss and associated costs. The model adopted mathematical associations between design variables and the overall installation cost of layers over a pipe from the available literature. The proposed model considered one or more insulation layers of rock wool and calcium silicate to oil pipelines containing steam, furfural, reduced crude or 300-distillate oil. All calculations considered fixed-charge rates as a fraction of 1 or 0.15. The results were compared with standard values and those predicted by other researchers in the literature. For the steam line, the standard insulation thickness was 50 mm, jumping to 327 mm for rock wool and 232 mm for calcium silicate. However, it decreased to 38 mm for double-layer calcium silicate and 138 mm for double-layer rock wool. For furfural, the insulation thickness was 40 mm, which rose to 159 mm for rock wool and 112 mm for calcium silicate. In general, for all four cases, the results show that using normal insulation thickness is inadequate and not economical. For example, for 300-distillate oil, the present practice puts the cost function at 54 USD/m, which drops to 20 USD/m for rock wool and 24 USD/m each for single-layer silicate and double-layer insulation. This amounts to almost 60% cost savings. Similar trends are observed for the other three cases. This model can provide up to 60% savings in cost and a 92% reduction in heat loss at optimum insulation thickness compared to other models.