Silica Fouling of Heat Transfer Equipment—Experiments and Model

Abstract

Silica fouling of heat transfer equipment in geothermal energy systems is studied. The effects of temperature, pH, and salinity on silica solubility and silica polymerization are reviewed. Experimental fouling data are presented for geothermal brines with different pH values, chemical compositions, and thermal-hydraulic conditions. The effects of supersaturation, pH, Reynolds number, and the concentration of ions in the brine solution on the formation of silica scale in the heat exchanger tube are discussed. A silica deposition model is proposed that accounts for supersaturation, pH factor, salinity, and thermal-hydraulic effects. Results based on the analytical model are compared with silica fouling data obtained in the laboratory as well as from two geothermal fields.

Modeling of calcium oxalate and amorphous silica composite fouling

“…A somewhat similar trend was observed for K R values in the Chan et al model (Figure 2), except that the latter gave more scatter around linearity, presumably because the effects of thermal hydraulic parameters other than heat flux were not considered in this model. The value of r in the Chan et al model was found to vary in the range of 0.8–0.95, which was in reasonable agreement with the published data of 1.0 17. The parameter values of K d / K 3 in the Watkinson–Epstein model are shown in Figure 3.…”

“…Chan et al17 derived a model that could predict the deposition of silica from geothermal brines in tubular heat exchangers as a function of silica supersaturation and pH where K R is the deposition rate constant, C b is the bulk concentration of silica, C e is the silica solubility at surface temperature, C is the molar concentration of OH − ions in the bulk solution, and p is the reaction order of silica. The thermal hydraulic conditions were accounted for in the model by evaluating their effects on surface temperature.…”

Statistical aspects of fouling processes

“…In addition to the 'trend' of the process, the superimposed 'scatter or uncertainty' of the process can best be characterized by such a 'probabilistic' representation of the fouling process as outlined in this paper. Both replicate laboratory experiments [14][15][16] in the study of fouling growth models and eld investigations suggest that there is a considerable scatter in the values of R f (t) at any time t and similarly, for any xed value of R f (t)ˆR fc , there will be a corresponding scatter in the values of t. Both 'trend and scatter' in R f (t) can be expressed by its probability distribution f [R f (t)]; main indicators of this distribution are its mean value m[R f (t)] re ecting the trend of the process and standard deviation s[R f (t)] representing the 'scatter or uncertainty' of the process. It is often desirable to discuss the scatter in terms of the non-dimensional scatter parameter de ned as the coef cient of variation, K[R f (t)]ˆs[R f (t)]/m[R f (t)] of the process.…”

Analysis of silica fouling on nonwetting surfaces

“…They observed that the precipitation of silica is strongly governed by the surface roughness; however, once silica has nucleated, further growth of and spread of aggregates is independent of surface morphology. Chan et al 7 systematically studied the effects of supersaturation and Reynolds number on the scale formation of silica. Several other studies focused on the chemistry of silica aggregate formation, surface–silica interaction and quantification of the extent of fouling on smooth surfaces.…”

“…Several studies have been reported on the fouling of conventional heat transfer surfaces by silica. [5][6][7][8][9][10] Heuvel et al 6 studied the effects of surface roughness on the early stages of silica scale formation. They observed that the precipitation of silica is strongly governed by the surface roughness; however, once silica has nucleated, further growth of and spread of aggregates is independent of surface morphology.…”