Black liquor from pulp mills is valuable biomass that can be recycled as a CO₂-neutral, renewable fuel. However, biomass combustion produces significant ash deposits reducing the overall process efficiency. A recovery boiler generally uses an ash deposit removal system (ADRS), but ADRS operation is inefficient, and the recycling efficiency of the biomass is decreased, leading to an increase in CO₂ emission. This work proposed an optimal operation of ADRS to improve the recycling efficiency of biomass for CO₂ emission reduction based on reinforcement learning. The optimal operation of the ADRS was derived by the following steps. 1) Real-time process operating data (i.e., temperatures of the flue gas, water, and steam) were gathered and a computational fluid dynamics model was developed to predict the flue gas temperature in the superheater section. 2) The decrease in the heat transfer rate was calculated using the gathered data to define a reward update matrix. 3) A modified Q-learning algorithm was developed based on the defined reward update matrix, and the algorithm was used to derive the Q-matrix, a function that predicted the expected dynamic reward (i.e., priority for ash deposit removal) of performing a given action (i.e., sootblowing) at a given state (i.e., each sootblowing location). 4) Using the obtained Q-matrix, the optimal operating sequence was derived. As a result, 22.58 ton/d of black liquor was saved and the CO₂ emission decreased by 755–1390 ton/y with an increase in the net profit by $1,010,000.

来自纸浆厂的黑液是有价值的生物质，可以作为 CO ₂中性的可再生燃料进行回收。然而，生物质燃烧会产生大量灰烬沉积物，从而降低整体工艺效率。回收锅炉一般采用除灰系统(ADRS)，但ADRS运行效率低，生物质的回收效率降低，导致CO ₂排放量增加。本工作提出了一种优化的 ADRS 操作，以提高生物质对 CO _{2的回收效率}基于强化学习的减排。ADRS 的最佳操作是通过以下步骤得出的。1）收集实时过程运行数据（即烟气、水和蒸汽的温度），并开发了计算流体动力学模型来预测过热器段的烟气温度。2) 使用收集到的数据计算传热率的降低，以定义奖励更新矩阵。3）基于定义的奖励更新矩阵开发了一种改进的Q-learning算法，并使用该算法推导出Q-matrix，这是一个预测执行a的预期动态奖励（即除灰优先级）的函数在给定状态（即每个吹灰位置）的给定动作（即吹灰）。4) 使用得到的 Q 矩阵，得出了最优的操作顺序。结果，节省了 22.58 吨/天的黑液和 CO₂排放量减少了 755–1390 吨/年，净利润增加了 1,010,000 美元。