废轮胎胶粒热解油气分离效率智慧控制数据

废橡胶胶粒无氧热解中，热解出口油气温度、热解出口油气压力、热解出口油气流量、冷凝塔压力、热解油喷淋流量、高闪点油产率、低闪点油产率会对热解油气分离效率产生影响。该智慧控制模型通过对油气分离效率数据的实时监测和分析，可以精确调整热解过程中的温度、压力、流量、闪点油产率等参数，优化热解工艺，使得热解油气分离效率稳定提升，最大限度地回收利用油气中的能量，提高能源利用效率，节能高效，符合绿色生产的环境保护要求，也对其他企业稳定提高热解油气分离效率提供指导意义，降低对传统能源和资源的依赖，减少废弃物的产生，实现经济、环境和社会效益的统一，推动可持续发展。步骤一：采集数据，通过DCS系统，采集热解出口油气温度、热解出口油气压力、热解出口油气流量、冷凝塔压力、热解油喷淋流量、热解油气分离效率、高闪点油产率、低闪点油产率数据，其中热解油气分离效率数据由DCS系统直接监测存储。步骤二：利用Eviews分析软件，构建多元线性回归模型，设热解出口油气温度为X1，热解出口油气压力为X2，热解出口油气流量为X3，冷凝塔压力为X4，热解油喷淋流量为X5，高闪点油产率为X6，低闪点油产率为X7，热解油气分离效率为y，设多元线性回归函数为y=β0+β1*X1+β2*X2+β3*X3+β4*X4+β5*X5+β6*X6+β7*X7，其中β0为截距项，β1、β2、β3、β4、β5、β6、β7为其回归系数，得出拟合优度最优解，以实时调整各参数，最大程度减少能源的消耗，在工艺稳定的同时使得热解油气分离效率得到提高。

During the anaerobic pyrolysis of waste rubber particles, the temperature, pressure and flow rate of pyrolysis outlet oil-gas, the pressure of condensation tower, the spray flow rate of pyrolysis oil, the yield of high-flash point oil and the yield of low-flash point oil all affect the separation efficiency of pyrolysis oil-gas. This intelligent control model, through real-time monitoring and analysis of oil-gas separation efficiency data, can precisely adjust parameters such as temperature, pressure, flow rate and flash point oil yield during the pyrolysis process, optimize the pyrolysis technology, steadily improve the separation efficiency of pyrolysis oil-gas, maximize the energy recovery from oil-gas, enhance energy utilization efficiency, achieve energy conservation and high efficiency, meet the environmental protection requirements of green production, provide guidance for other enterprises to stably improve the separation efficiency of pyrolysis oil-gas, reduce dependence on traditional energy and resources, cut down waste generation, realize the unity of economic, environmental and social benefits, and promote sustainable development. Step 1: Data Collection. Data including the temperature, pressure and flow rate of pyrolysis outlet oil-gas, the pressure of condensation tower, the spray flow rate of pyrolysis oil, the separation efficiency of pyrolysis oil-gas, the yield of high-flash point oil and the yield of low-flash point oil are collected via the DCS (Distributed Control System). Among them, the pyrolysis oil-gas separation efficiency data is directly monitored and stored by the DCS system. Step 2: Model Construction. Using Eviews analysis software, a multiple linear regression model is established. Let the temperature of pyrolysis outlet oil-gas be X₁, the pressure of pyrolysis outlet oil-gas be X₂, the flow rate of pyrolysis outlet oil-gas be X₃, the pressure of condensation tower be X₄, the spray flow rate of pyrolysis oil be X₅, the yield of high-flash point oil be X₆, the yield of low-flash point oil be X₇, and the separation efficiency of pyrolysis oil-gas be y. The multiple linear regression function is defined as: $y = eta_0 + eta_1 X_1 + eta_2 X_2 + eta_3 X_3 + eta_4 X_4 + eta_5 X_5 + eta_6 X_6 + eta_7 X_7$, where $eta_0$ is the intercept term, and $eta_1, eta_2, eta_3, eta_4, eta_5, eta_6, eta_7$ are the regression coefficients. The optimal solution of goodness of fit is obtained to adjust each parameter in real time, minimize energy consumption, stabilize the process while improving the separation efficiency of pyrolysis oil-gas.