Radial　basis　function　(RBF)　networks,　because　of　their　universal　approximation　ability,　have　been　widely　applied　to　industrial　process　modeling.　In　this　study,　an　Improved　ErrCor　(IErrCor)　algorithm-an　extension　of　error　correction　(ErrCor)　algorithm-is　proposed,　in　which　compact　structure　and　satisfactory　generalization　ability　can　be　obtained　with　only　one　learning　try.　First,　a　second-order-based　constructive　mechanism　guarantees　the　structure　compactness　and　computational　efficiency.　Second,　different　from　other　algorithms　that　start　with　random　or　constant　parameters,　optimal　initial　parameters　accelerate　the　convergence　process　and　improve　the　convergence　performance,　making　the　IErrCor　RBF　network　more　stable.　Convergence　analysis　is　given　to　demonstrate　and　prove　the　reasonability　and　effectiveness　of　the　proposed　algorithm.　Finally,　the　IErrCor　algorithm　has　been　evaluated　and　compared　with　several　popular　advanced　learning　algorithms　such　as　support　vector　machine　(SVM),　extreme　learning　machine　(ELM),　and　original　ErrCor　algorithm　through　a　series　of　benchmark　experiments　and　then　been　applied　to　effluent　water　quality　prediction　in　wastewater　treatment　process.　All　the　simulation　results　reveal　the　outperformance　and　potentiality　of　IErrCor　RBF　network　in　industrial　applications.