本文選題：煤泥水處理 + 藥劑優(yōu)化��； 參考：《太原理工大學》2017年碩士論文

【摘要】：煤泥水處理作為選煤廠的一項重要工藝流程,直接關系到廠區(qū)的閉水循環(huán)指標,同時也對選煤廠整體效率與生產指標造成影響。濃縮與壓濾是煤泥水處理中的兩個關鍵環(huán)節(jié),其目的就是為了實現煤泥水中細微顆粒與清水的分離,回收煤泥,清水循環(huán)利用。絮凝劑與助濾劑的添加主要是為了改變煤泥水微粒的表面電性,加速煤泥微粒絮團的形成,加速沉淀,并提高脫水性能。本文針對成莊礦選煤廠原有絮凝劑與助濾劑藥劑添加裝置系統(tǒng)中添加量由人工設置或針對單個環(huán)節(jié)進行添加量控制的問題,沒有兼顧濃縮與壓濾兩個相關環(huán)節(jié)的協同作用,造成藥劑添加不合理,造成藥劑的浪費,為了解決上述問題,提出了針對濃縮與壓濾過程藥劑協同控制的研究。通過對原有絮凝劑與助濾劑藥劑添加裝置的分析,現具有完善的藥劑溶液制備裝置與自動添加裝置,藥劑添加量控制策略有待完善。系統(tǒng)利用原有的制備與添加裝置,藥劑添加量由協同系統(tǒng)求解所得。煤泥水處理過程作為一個典型的物化反應過程,在濃縮與壓濾過程中藥劑添加量主要影響變量為入料濃度、入料流量、底流濃度、溢流濃度、壓濾周期與煤泥餅水分,該過程具有強耦合、非線性、大滯后等特點,很難通過數學推導建立其模型,本文提出了通過BP神經網絡對藥劑添加量進行模型的建立,并通過APSO算法進行最優(yōu)量求解的策略。經過對BP神經網絡原理和推理算法的分析,基于煤泥水處理中變量影響關系,分別建立4×5×1結構的絮凝劑添加模型與3×5×1結構的助濾劑神經網絡模型,并利用現場50組數據對網絡訓練。依據要實現目標與現場工況建立藥劑添加優(yōu)化量最優(yōu)化模型,確定優(yōu)化約束條件,選定PSO算法對藥劑最優(yōu)化模型進行求解計算,并利用慣性權重值與粒子飛翔速度線性遞減的自適應策略對PSO算法進行改進。在Matlab平臺對APSO算法進行程序的設計與運行。為了實現算法對優(yōu)化量的在線求解計算,通過Simulink仿真平臺利用S函數調用APSO程序,并通過OPC技術實現與PLC控制器的聯合運行。本系統(tǒng)選用AB 1756-Control Logix PLC為協同控制器,研華科技ACP4000作為上位機,系統(tǒng)進行硬件結構的搭建,使用RSLogix5000進行控制器程序的編寫,選用Matlab/Simulink作為APSO算法在線計算平臺。為了實現數據的交互,使用MSG功能模塊與原系統(tǒng)控制器進行通訊,利用OPC接口技術實現控制器與Matlab、FT VIEW之間的數據通訊。系統(tǒng)通過現場傳感器采集工況數據,通過OPC技術反饋到Matlab的APSO算法相應變量中,由算法在線對該工況下最優(yōu)藥劑添加量求解計算,將優(yōu)化藥劑量經協同控制器返回給原藥劑添加系統(tǒng),之后原藥劑添加系統(tǒng)按優(yōu)化藥劑量執(zhí)行動作,達到在線協同優(yōu)化的目的。系統(tǒng)在成莊礦選煤廠運行穩(wěn)定可靠,且通過對系統(tǒng)運行前后三個月的數據分析,煤泥生產總量也略有提高,同時噸煤泥PAC藥耗由2.453Kg/T降低到2.341Kg/T,噸煤泥PAM藥耗由0.182Kg/T降低到0.172Kg/T。藥劑消耗的經濟指標,由4.119降低到3.914。說明本系統(tǒng)不僅保證了煤泥水處理系統(tǒng)生產速率,同時降低了藥劑的消耗,提高了藥劑間的協同作用與選煤廠經濟效益。
[Abstract]:Slime water treatment is an important process of coal preparation plant, is directly related to the closed water circulation index of the plant, but also the impact on the overall efficiency of coal preparation plant and production index. Concentration and filter press are two key links of slime water treatment, its purpose is to realize the separation of fine particles and water in coal slurry recycling water slime, recycling. Adding flocculant and filter aid is mainly to change the surface electric properties of coal slurry particles, the formation of slime particles, accelerate floc accelerated precipitation, and improve the dewatering performance. This paper focused on the effects of Chengzhuang Coal Preparation Plant original flocculant and filter aid agent adding device system by artificial set or for a single link to add volume control problems, did not take into account the synergistic effect of the two related aspects of concentration and filter press, by adding medicament is not reasonable, resulting in chemical waste, for To solve the above problems, put forward in the research process of the drug concentration and filter press cooperative control. Through the analysis of the original flocculant adding device and filter aid agent, reagent solution preparation device has perfect device and automatic adding reagent addition, control strategy needs to be improved. The system uses the original preparation and adding device the reagent addition by solving the cooperative system. The slime water treatment process as a typical chemical reaction process, adding amount of main influence variable feeding concentration in concentration and filtration process of traditional Chinese medicine agent, feeding rate, bottom flow concentration, overflow concentration, filtration cycle and slime cake moisture, the process with strong coupling. The characteristics of nonlinear, large delay, it is difficult to establish the model through mathematical derivation, this paper proposes to establish the BP neural network of the reagent addition model, and the optimal APSO algorithm The amount of solving strategy. Through the analysis of the BP neural network principle and inference algorithm, variable slime water treatment effect based on the established 4 * 5 * 1 flocculant structure filter aid addition neural network model with 3 x 5 x 1 structure, and using the data of the 50 groups on the basis of network training. To achieve the goal of establishing pharmacy and site conditions add optimization optimization model to determine the optimal conditions, selected PSO algorithm to solve this optimization model of agents, and the use of inertia weight value to improve the PSO algorithm and the particle velocity decreases linearly flying adaptive strategy. In the design and operation of the Matlab platform program of APSO algorithm. In order to on line optimization algorithm of computing quantity, by using the S function call APSO program Simulink simulation platform, and through the implementation of joint operation with the PLC controller of OPC technology. This system The AB 1756-Control Logix PLC for collaborative controller, Advantech ACP4000 as the host computer, the hardware structure of the system was set up, prepared using RSLogix5000 controller program, Matlab/Simulink is selected as the APSO algorithm of online computing platform. In order to realize data exchange, MSG module is used to communicate with the original system controller, controller and Matlab using OPC interface technology, data communication between FT VIEW. This system uses the sensor to collect performance data by OPC technology, feedback to the Matlab APSO algorithm the corresponding variables, by the method of online optimal agent under the condition, adding amount of calculation, the optimization dosage by synergetic controller is returned to the technical agent system, after the original agent according to the optimized dosage action, achieve online collaborative optimization. System in Chengzhuang coal mine coal preparation plant stable operation Reliable, and through the analysis of the data of three months before and after the operation system, the total coal production also increased slightly, while PAC tons of slime drug consumption decreased from 2.453Kg/T to 2.341Kg/T, the economic index decreased from 0.182Kg/T to 0.172Kg/T. pharmaceutical consumption consumption tons of slime PAM drugs, reduced from 4.119 to 3.914. that the system not only ensured the production rate coal slurry treatment, and reduce drug consumption, improve the pesticides and the synergy between coal preparation plant and economic benefits.

【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TD94

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本文編號：1753470