A　learner　model　with　fast　learning　and　compact　architecture　is　expected　for　industrial　data　modeling.　To　achieve　these　goals　during　stochastic　configuration　networks　(SCNs)　con-struction,　we　propose　an　improved　version　of　SCNs　in　this　paper.　Unlike　the　original　SCNs,　the　improved　one　employs　a　new　inequality　constraint　in　the　construction　process.　In　addition,　to　speed　up　the　construction　efficiency　of　SCNs,　a　node　selection　method　is　pro-posed　to　adaptively　select　nodes　from　a　candidate　pool.　Moreover,　to　reduce　the　redundant　nodes　of　the　built　SCNs　model,　we　further　compress　the　model　based　on　the　singular　value　decomposition　algorithm.　The　improved　SCNs　are　compared　with　other　methods　over　four　datasets　and　then　applied　to　the　ammonia-nitrogen　concentration　prediction　task　in　the　wastewater　treatment　process.　Experimental　results　indicate　that　the　proposed　method　has　good　potential　for　industrial　data　analytics.(c)　2022　Elsevier　Inc.　All　rights　reserved.