The　process　of　smelting　non-ferrous　metals　results　in　significant　emissions　of　flue　gas　that　contains　sulfur　dioxide　(SO　2$$　{}_2　$$),　which　is　very　harmful　to　the　environment.　Through　precise　control　of　converter　inlet　temperature,　it　is　feasible　to　enhance　the　conversion　ratio　of　SO　2$$　{}_2　$$　and　simultaneously　mitigate　environmental　pollution　by　generating　acid　from　flue　gas.　Because　of　the　high　degree　of　uncertainty　in　smelting　process,　converter　inlet　temperature　is　challenging　to　regulate　and　controller　frequently　needs　updating.　To　improve　control　performance　and　decrease　controller　update　times,　an　event-triggered　neural　network　model　predictive　control　(ETNMPC)　strategy　is　proposed.　First,　long　short-term　memory　(LSTM)　prediction　model　and　model　predictive　controller　are　developed.　Second,　it　is　decided　whether　to　update　the　existing　controller　by　designing　an　event-triggered　mechanism.　Finally,　using　real　data　from　a　copper　facility　in　Jiangxi　Province,　the　temperature　control　experiment　of　converter　inlet　is　carried　out.　Simulation　results　demonstrate　that　the　proposed　ETNMPC　outperforms　conventional　time-triggered　method　in　terms　of　control　performance,　greatly　lowers　the　times　of　controller　updates,　and　significantly　lowers　computation　costs　and　communication　burden.　Key　findings:　(1)　LSTM　neural　network　is　used　to　establish　the　predictive　model　of　converter　inlet　temperature　and　model　predictive　controller　is　designed.　(2)　Two　different　event-triggered　mechanisms　with　fixed　threshold　are　designed.　(3)　Event-triggered　neural　network　predictive　control　can　effectively　reduce　the　number　of　controller　triggers,　save　computing　resources,　and　improve　system　performance.　image