钢铁企业煤气系统预测及优化调度研究
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摘要
钢铁企业煤气系统与主生产系统紧密耦合,当生产工况发生变化时,煤气的发生量、消耗量也会随之变化,规律性弱,依靠人工经验无法对其发生量、消耗量进行准确的预测,从而直接导致调度的滞后,考虑到大部分工况都是事后调度,而这在一定程度上会造成能源的严重浪费,尤其当有些工况出现时若发生事后调度时间过长,更会给煤气系统稳定高效运行带来压力。为解决上述问题,论文从煤气的半生命周期出发,系统全面地识别和评估影响煤气利用的各类因素,采用逐层递进优化的建模思路,建立了煤气系统分类预测模型,在预测模型的基础上从煤气系统全局出发,以固定用户、可变用户消耗燃料量最小为目标,考虑缓冲用户消耗煤气特性,采用规则调度与概率调度,建立优化调度模型；并基于模型研究了应用企业的煤气利用问题。具体研究内容如下：
(1)在准确识别钢铁企业煤气发生、消耗影响因素的基础上,针对机理模型预测精度不高的实际情况,建立了SVC-HP-ENN-LSSVM-MC分类预测模型。整个建模过程采用预测前模型识别,预测中针对数据性质分解数据进行预测,预测后进行修正的方式。基于钢铁企业煤气发生、消耗频繁波动特性,首先利用SVC对煤气量进行工况分类,对不同工况建立不同模型进行预测。为描述不同工况煤气发生、消耗特性本文提出利用HP滤波把原始数据序列分为趋势序列和波动序列,融合Elman神经网络和最小二乘支持向量机优势分别对趋势序列和波动序列建立预测模型进行预测。并对预测后的残差序列,引入马尔科夫链状态转移矩阵进行修正。利用钢铁企业实际数据对所建模型进行Wilcoxon符号秩检验表明：在离线建立分类模型的基础上,建立不同工况的预测模型,能够反映所有工况,不仅减少了在线训练时间,而且可有效提高预测精度,尤其是当工况改变时,效果更佳。
(2)依据钢铁企业煤气用户产消特性及煤气产生、消耗的预测结果,构建煤气系统优化调度模型。综合考虑煤气用户的热值要求、煤气混合熵增引起的能量损失及缓冲用户的缓冲能力等,以燃料消耗量最小为目标,遵循按质用能,达到能源梯级利用的目的,使能源结构趋于最优化,在此基础上建立优化调度模型。同时为减少计算过程的复杂程度,在调度过程中主要考虑固定用户、可变用户和缓冲用户,对于转换用户不纳入计算。本文所建立的煤气系统优化调度模型改变了以往单纯考虑固定用户、可变用户,而不考虑缓冲用户特性的缺陷。
(3)为使煤气系统全局能源利用效率最高,本文提出两种建模方法对缓冲用户消耗煤气量进行调整。方法一：根据自备电厂锅炉工作特性,建立燃料消耗与锅炉负荷间的关系模型,从而得到锅炉运行经济区域,并在此基础上,建立缓冲用户煤气优化调度规则,保证煤气柜在安全范围内运行,并使锅炉在经济区域或靠近经济区域运行;方法二：在缓冲用户经济效益目标函数中采用变权惩罚函数处理能源成本和运行风险间的关系,提出了协调缓冲用户煤气调度的经济性和运行风险间关系的概率模型。通过建立锅炉变权惩罚函数、煤气柜柜位变权惩罚函数,能科学合理地描述调度情况,有效避免常规多目标优化时人为定权重与实际煤气波动情况相距甚远,导致对惩罚估计偏差较大,影响正确决策。
本论文建立了钢铁企业煤气系统预测和优化调度模型。所建预测模型与其它模型相比具有较高的预测精度,并且具有适应工况时变的能力,煤气量预测平均相对误差都小于或等于2.3%,并通过了Wilcoxon符号秩检验,满足工业生产需要。针对典型工况得到的调度方案合理、实用,调度结果表明：运用所建预测调度模型,应用于钢铁企业正常生产工况将节约煤气折标煤16.63kgce/t钢,按此计算一年将节约197956.29toe,节能潜力巨大。
Gas system of iron and steel enterprises enjoys close coupling with main production system. With the change of production conditions, occurrence and consumption of gas will change accordingly but with small regularity. Personal experiences can not predict gas consumption accurately, which leads to the backwardness of management. Given that most production condition are controlled after the change, especially when it takes long time to control it, it will cause huge waste of energy, and bring high pressure on the operation of gas system. In order to solve the above problems, starting from the half life circle of gas, this paper systematically defines and evaluates factors affecting gas utilization. It adopts the thinking of gradual optimization and establishes a prediction model of classifying gas system. With this prediction model, this paper takes a perspective of overall situation of gas system, targeting at the minimum consumption of fixed and variable users. Taking the characteristics of potential users'using gas into consideration, a model of optimized scheduling is constructed. Gas utilization of enterprises is explored based on the scheduling model. Detailed investigation is as follows.
(1) Based on the identification of factors affecting gas occurrence and consumption in iron and steel enterprises, a SVC-HP-ENN-LSSVM-MC classification prediction model is established to cope with the reality of little prediction accuracy of the mechanistic model. The whole modeling process adopts the recognition of pre-prediction model, that is, data is predicted based on their qualities and then modified correspondingly. The occurrence and consumption of gas in iron and steel enterprises fluctuate frequently. SVC is used to classify the production condition and different prediction models are built to deal with different production conditions. HP wave filtering is used to divide original data sequence into trend and volatility series. Elman neural network and advantages of least square support vector machine are combined to build a prediction model. After prediction, Markov transition matrix is introduced to modify residual series. Wilcoxon rank test is conducted to examine data of iron and steel enterprises. Results show that:based on offline classification model, prediction models reflecting all production conditions are established, which not only reduces online training time, but also improves the accuracy of prediction, especially when the production condition changes, the outcome is more satisfactory.
(2) Based on consumption characteristics of gas users and prediction results of gas occurrence and consumption in iron and steel enterprises, the paper builds a optimized scheduling model of gas system based on factors like heat demand by gas users, energy loss in gas mixing entropy and users' interruptible capacity. It aims at the minimum fuel consumption. In line with its quality, energy cascade utilization is realized. Meanwhile, in order to reduce the computational complexity, fixed users, variable users and interruptible users are taken into calculation except transferred users. This model compensates the drawback of ignoring interruptible users.
(3) In order to realize the utmost utilization of gas as a whole, this paper presents two modeling approaches for interruptible users to adjust the consumed amount of gas. Method1:according to the working features of captive power plant boilers, a model reflecting the relation between fuel consumption and boiler load is built to acquire boilers' economic area. Based on this model, optimal scheduling rules to ensure gas operates with safety within the economic area is set up for interruptible users. Method2:in the objective function of interruptible users'economic benefits, variable weight penalty function is adopted to deal with the relation between energy cost and operational risks. It presents probability model coordinating the relationship between economic quality of scheduling and operational risks for interruptible users. Boilers' variable weight penalty function and gas tank counter variable weight penalty function, are constructed to scientifically and reasonably depict scheduling situation, which effectively avoids the shortage of conventional multi-objective optimization that artificially fixed weights is far from the actual gas fluctuation, leading to large deviation of penalty estimation and making hcorrect decisions.
The paper establishes prediction and optimized scheduling models of gas system in iron and steel enterprises. The prediction model enjoys high accuracy than other models, and has the capability to adapt to production condition's change. The average relative error of gas consumption prediction is less than or equal to2.3%, and endures Wilcoxon rank test, which meets the need of industrial production. For typical operating conditions, the scheduling scheme is reasonable and practical. Results show that:if the prediction and scheduling models apply in iron and steel enterprises with normal production conditions, it will save gas about16.63kgce/t steel and save197956.29tce per year, which enjoys broad prospects for energy saving.
(1)在准确识别钢铁企业煤气发生、消耗影响因素的基础上,针对机理模型预测精度不高的实际情况,建立了SVC-HP-ENN-LSSVM-MC分类预测模型。整个建模过程采用预测前模型识别,预测中针对数据性质分解数据进行预测,预测后进行修正的方式。基于钢铁企业煤气发生、消耗频繁波动特性,首先利用SVC对煤气量进行工况分类,对不同工况建立不同模型进行预测。为描述不同工况煤气发生、消耗特性本文提出利用HP滤波把原始数据序列分为趋势序列和波动序列,融合Elman神经网络和最小二乘支持向量机优势分别对趋势序列和波动序列建立预测模型进行预测。并对预测后的残差序列,引入马尔科夫链状态转移矩阵进行修正。利用钢铁企业实际数据对所建模型进行Wilcoxon符号秩检验表明：在离线建立分类模型的基础上,建立不同工况的预测模型,能够反映所有工况,不仅减少了在线训练时间,而且可有效提高预测精度,尤其是当工况改变时,效果更佳。
(2)依据钢铁企业煤气用户产消特性及煤气产生、消耗的预测结果,构建煤气系统优化调度模型。综合考虑煤气用户的热值要求、煤气混合熵增引起的能量损失及缓冲用户的缓冲能力等,以燃料消耗量最小为目标,遵循按质用能,达到能源梯级利用的目的,使能源结构趋于最优化,在此基础上建立优化调度模型。同时为减少计算过程的复杂程度,在调度过程中主要考虑固定用户、可变用户和缓冲用户,对于转换用户不纳入计算。本文所建立的煤气系统优化调度模型改变了以往单纯考虑固定用户、可变用户,而不考虑缓冲用户特性的缺陷。
(3)为使煤气系统全局能源利用效率最高,本文提出两种建模方法对缓冲用户消耗煤气量进行调整。方法一：根据自备电厂锅炉工作特性,建立燃料消耗与锅炉负荷间的关系模型,从而得到锅炉运行经济区域,并在此基础上,建立缓冲用户煤气优化调度规则,保证煤气柜在安全范围内运行,并使锅炉在经济区域或靠近经济区域运行;方法二：在缓冲用户经济效益目标函数中采用变权惩罚函数处理能源成本和运行风险间的关系,提出了协调缓冲用户煤气调度的经济性和运行风险间关系的概率模型。通过建立锅炉变权惩罚函数、煤气柜柜位变权惩罚函数,能科学合理地描述调度情况,有效避免常规多目标优化时人为定权重与实际煤气波动情况相距甚远,导致对惩罚估计偏差较大,影响正确决策。
本论文建立了钢铁企业煤气系统预测和优化调度模型。所建预测模型与其它模型相比具有较高的预测精度,并且具有适应工况时变的能力,煤气量预测平均相对误差都小于或等于2.3%,并通过了Wilcoxon符号秩检验,满足工业生产需要。针对典型工况得到的调度方案合理、实用,调度结果表明：运用所建预测调度模型,应用于钢铁企业正常生产工况将节约煤气折标煤16.63kgce/t钢,按此计算一年将节约197956.29toe,节能潜力巨大。
Gas system of iron and steel enterprises enjoys close coupling with main production system. With the change of production conditions, occurrence and consumption of gas will change accordingly but with small regularity. Personal experiences can not predict gas consumption accurately, which leads to the backwardness of management. Given that most production condition are controlled after the change, especially when it takes long time to control it, it will cause huge waste of energy, and bring high pressure on the operation of gas system. In order to solve the above problems, starting from the half life circle of gas, this paper systematically defines and evaluates factors affecting gas utilization. It adopts the thinking of gradual optimization and establishes a prediction model of classifying gas system. With this prediction model, this paper takes a perspective of overall situation of gas system, targeting at the minimum consumption of fixed and variable users. Taking the characteristics of potential users'using gas into consideration, a model of optimized scheduling is constructed. Gas utilization of enterprises is explored based on the scheduling model. Detailed investigation is as follows.
(1) Based on the identification of factors affecting gas occurrence and consumption in iron and steel enterprises, a SVC-HP-ENN-LSSVM-MC classification prediction model is established to cope with the reality of little prediction accuracy of the mechanistic model. The whole modeling process adopts the recognition of pre-prediction model, that is, data is predicted based on their qualities and then modified correspondingly. The occurrence and consumption of gas in iron and steel enterprises fluctuate frequently. SVC is used to classify the production condition and different prediction models are built to deal with different production conditions. HP wave filtering is used to divide original data sequence into trend and volatility series. Elman neural network and advantages of least square support vector machine are combined to build a prediction model. After prediction, Markov transition matrix is introduced to modify residual series. Wilcoxon rank test is conducted to examine data of iron and steel enterprises. Results show that:based on offline classification model, prediction models reflecting all production conditions are established, which not only reduces online training time, but also improves the accuracy of prediction, especially when the production condition changes, the outcome is more satisfactory.
(2) Based on consumption characteristics of gas users and prediction results of gas occurrence and consumption in iron and steel enterprises, the paper builds a optimized scheduling model of gas system based on factors like heat demand by gas users, energy loss in gas mixing entropy and users' interruptible capacity. It aims at the minimum fuel consumption. In line with its quality, energy cascade utilization is realized. Meanwhile, in order to reduce the computational complexity, fixed users, variable users and interruptible users are taken into calculation except transferred users. This model compensates the drawback of ignoring interruptible users.
(3) In order to realize the utmost utilization of gas as a whole, this paper presents two modeling approaches for interruptible users to adjust the consumed amount of gas. Method1:according to the working features of captive power plant boilers, a model reflecting the relation between fuel consumption and boiler load is built to acquire boilers' economic area. Based on this model, optimal scheduling rules to ensure gas operates with safety within the economic area is set up for interruptible users. Method2:in the objective function of interruptible users'economic benefits, variable weight penalty function is adopted to deal with the relation between energy cost and operational risks. It presents probability model coordinating the relationship between economic quality of scheduling and operational risks for interruptible users. Boilers' variable weight penalty function and gas tank counter variable weight penalty function, are constructed to scientifically and reasonably depict scheduling situation, which effectively avoids the shortage of conventional multi-objective optimization that artificially fixed weights is far from the actual gas fluctuation, leading to large deviation of penalty estimation and making hcorrect decisions.
The paper establishes prediction and optimized scheduling models of gas system in iron and steel enterprises. The prediction model enjoys high accuracy than other models, and has the capability to adapt to production condition's change. The average relative error of gas consumption prediction is less than or equal to2.3%, and endures Wilcoxon rank test, which meets the need of industrial production. For typical operating conditions, the scheduling scheme is reasonable and practical. Results show that:if the prediction and scheduling models apply in iron and steel enterprises with normal production conditions, it will save gas about16.63kgce/t steel and save197956.29tce per year, which enjoys broad prospects for energy saving.
引文
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