Development of optimization software to improve the efficiencies of desalination and wastewater treatment

Pani能源正在开发一种新的膜工艺设计技术和优化软件（“Pani Digital”），以提高海水淡化或污水处理厂等水工艺的成本效率。过程设计技术的一个关键组成部分是实时响应控制系统，该系统确保基于工厂部件的计算模型的最佳操作。模型组件指定控制组件关闭/打开时间表（每小时更新率）的工厂操作规则。初步的建模工作突出了一些关键的技术开发挑战：1)自由控制变量的数量；2)控制方程的数量和复杂度；3)非线性方程和隐式方程的混合；4)组件退化引起的时间依赖性。传统的基于物理模型的优化技术难以解决这类问题。预测分析使用历史工厂数据以及分析、统计和机器学习技术，可以提供更有效的方法来控制所需的优化。在预测分析工作流中，时间从基础层面进入模型。监督式机器学习技术可以采用可控和可观察/不可观察变量来模拟工厂运行，从而实现对工厂的有效控制。

Pani Energy is developing a novel membrane process design technology and optimization software ("Pani Digital") that enhances cost efficiencies of water processes such as desalination or wastewater treatment plants. A key component of the process design technology is a real-time responsive control system that ensures optimal operations based on computational models of plant components. The model components specify the plant operating rules which control component off/on schedules (on hourly update rates). Preliminary modeling work has highlighted a number of key technical development challenges: 1) the number of free control variables; 2) the number and complexity of the governing equations; 3) a mixture of non-linear and implicit equations, and; 4) time dependency due to component degradation. Traditional physical model-based optimization techniques are challenged to solve this type of problem. Predictive analytics, using historical plant data along with analysis, statistics, and machine learning techniques, may provide a more effective means to control the optimizations required. In a predictive analytics workflow, time enters the model on a fundamental level. Supervised machine learning techniques can take both controllable and observable/unobservable variables to simulate plant operation, and thereby enable effective control of the plant.