This　paper　proposes　a　heuristic　dynamic　programming　(HDP)　scheme　to　simultaneously　control　the　dissolved　oxygen　concentration　and　the　nitrate　level　in　wastewater　treatment　processes　(WWTP).　Unlike　traditional　HDP　schemes,　the　optimal　control　values　are　calculated　in　an　analytical　way　by　the　proposed　HDP　controller.　It　can　reduce　the　learning　burden　of　the　HDP　controller　to　a　great　extent.　The　system　model　and　the　evaluation　index　J　are　approximated　by　two　echo　state　networks　(ESNs).　Gradient-based　learning　algorithms　are　employed　to　train　both　ESNs　online,　and　the　convergence　of　the　training　algorithm　is　investigated　based　on　Lyapunov　theory.　The　performance　of　the　proposed　ESN-based　HDP　(E-HDP)　controller　is　tested　and　evaluated　on　a　WWTP　benchmark.　Experimental　results　demonstrate　that　the　proposed　approach　can　achieve　effective　performance.