The　single-step　prediction　of　the　effluent　biochemical　oxygen　demand　(BOD)　in　wastewater　treatment　process　(WWTP)　cannot　provide　the　information　for　future　trends,　which　will　affect　the　decision-making　for　subsequent　control　strategies　in　WWTP.　To　solve　this　problem,　a　radial　basis　function　(RBF)　neural　network　soft-sensing　model　based　on　PSO　algorithm　(PSO-RBF)　is　proposed　for　multi-step　prediction　of　the　biochemical　oxygen　demand　(BOD)　in　WWTP.　First,　we　propose　an　RBF　model　for　multi-step　prediction　based　on　MIMO　strategy.　Then,　the　PSO　algorithm　is　used　to　optimize　the　RBF　parameters.　Finally,　the　proposed　model　is　applied　to　a　four-step　BOD　prediction　in　WWTP.　Experimental　results　show　that　this　model　can　better　predict　BOD　in　multiple　steps　compared　to　traditional　models.　©　2021　IEEE