不确定性下区域规划环评方案优化方法框架研究

为了弥补以往规划环评方案中存在的两个缺陷,构建了不确定性条件下融合型和调整型区域规划环评方案优化方法框架,从而消除规划方案本身潜在的环境影响,且实现了规划方案和环境保护补救措施的系统优化及其不确定性风险决策.同时,从方法学上,基于强化区间优化模型建立了不确定性条件下规划方案和补救措施的双层优化方法.最后,针对郑州市土地利用总体规划(1997～2010年)的规划环评,以生态系统服务功能价值的最大化为目标,以郑州市土地资源、经济发展(GDP)和环境指标(土地承载COD排放总量)为约束条件,得到不同风险水平下土地利用规划优化方案.结果表明:①优化后的规划方案的生态系统服务功能价值高于原规划方案,约为原规划方案的1.45～1.52倍,且完全能满足郑州市2010年GDP目标;②有必要在未来融合型或调整型规划环评融入不确定性优化思想,且需要为决策者提供不同环境风险水平下的决策方案.     

Ecosystem-inspired model and artificial intelligence predicts pollutant consumption capacity by coagulation in drinking water treatment

Conventional methods for water and wastewater treatment are energy-intensive, notably at the stage of coagulation–flocculation, calling for new strategies to predict pollutant reduction because the amount of energy consumed is related to how much of the pollutant is treated. Here we developed a model, named Bio-logic, inspired by ecosystems, where pollutants represent organisms, coagulants are food, and the wider environmental conditions are the living environment. Artificial intelligence was used to learn the biological behavior, which enabled an accurate prediction of the amount of pollutant reduction. Results show that pseudo-biological objects that have a strong affinity for biological food, such as turbidity, total phosphorus, ammonia nitrogen and the potassium permanganate index, induced a strong correlation, between measured pollutant consumption capacity and predicted values. For instance, R² correlation coefficients are 0.97 for turbidity and 0.92 for the potassium permanganate index in the laboratory; and 0.99 for turbidity, 0.90 for total phosphorus, 0.75 for ammonia nitrogen and 0.63 for the potassium permanganate index in water treatment plants. Overall, our findings demonstrate that artificial intelligence can use the water Bio-logic model to predict the pollutant consumption capacity.

     

A flocculation tensor to monitor water quality using a deep learning model

The increasing quantities of polluted waters are calling for advanced purification methods. Flocculation is an essential component of the water purification process, yet flocculation is commonly not optimal due to our poor understanding of the flocculation process. In particular, there is little knowledge on the mechanisms ruling the migration of pollutants during treatment. Here we have created the first tensor diagram, a mathematical framework for the flocculation process, analyzed its properties with a deep learning model, and developed a classification scheme for its relationship with pollutants. The tensor was constructed by combining pixel matrices from a variety of floc images, each with a particular flocculation period. Changing the factors used to make flocs images, such as coagulant dose and pH, resulted in tensors, which were used to generate matrices, that is the tensor diagram. Our deep learning algorithm employed a tensor diagram to identify pollution levels. Results show tensor map attributes with over 98% of sample images correctly classified. This approach offers potential to reduce the time delay of feedback from the flocculation process with deep learning categorization based on its clustering capabilities. The advantage of the tensor data from the flocculation process improves the efficiency and speed of response for commercial water treatment.