自來水廠混凝劑投加控制系統(tǒng)
產品介紹
給水廠混凝劑投加控制系統(tǒng)包含了混凝劑投加主控裝置、智能化混凝劑投加軟件包,對給水廠混凝加藥環(huán)節(jié)進行精簡模型,實時計算混凝劑投加量,實現(xiàn)混凝劑的精準投加。系統(tǒng)可用于給水廠的鋁鹽投加、鐵鹽投加等加藥場景。
技術原理
系統(tǒng)通過采集進水流量、進水濁度為前饋數(shù)據(jù),以出水濁度為反饋數(shù)據(jù),同時以進水pH為次級控制參數(shù),通過數(shù)據(jù)采集系統(tǒng)篩選真實有效的數(shù)據(jù),進行混凝劑投加量計算,根據(jù)計算結果控制加藥泵運行,動態(tài)控制混凝劑投加量,保障出水濁度達標。利用神經元算法,基于歷史數(shù)據(jù)與系統(tǒng)的自學習功能,進一步提高藥劑投加的精準性。
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)
給水廠混凝劑投加控制系統(tǒng)控制原理圖
應用效果
(1)實現(xiàn)混凝劑加藥的精細化。通過前饋+反饋+模型+歷史修正,實現(xiàn)藥劑投加的精細化控制,降低藥耗。
(2)穩(wěn)定出水水質。通過實時采集數(shù)據(jù)、計算投加量,提高系統(tǒng)靈敏度,減少時滯,穩(wěn)定出水濁度。
(3)實現(xiàn)全自動運行。系統(tǒng)閉環(huán)控制,減少人工干預、降低人員操作強度。