Department of Chemical Engineering, Research and Innovation Center on CO(2) and H(2) (RICH), Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates.
Department of Chemical Engineering, Research and Innovation Center on CO(2) and H(2) (RICH), Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates.
Water Res. 2020 May 1;174:115599. doi: 10.1016/j.watres.2020.115599. Epub 2020 Feb 8.
The optimal automatic start-up of anaerobic digesters has remained an elusive problem over the years to be solved at the lowest possible costs, including that of process monitoring. In this work, a non-linear model predictive control (NMPC) system was developed, under two proposed configurations, for the optimal start-up of anaerobic digesters treating soluble non-recalcitrant substrates. The minimum set of low cost practical control variables (CVs) selected for process start-up include (i) the effluent quality as acetate COD, (ii) the level of aceticlastic methanogenic biomass in the reactor, and (iii) the methane production rate (only for one of the NMPC configurations). The manipulated variables (MVs) consist of the volumetric inflow rates of the organic substrate, dilution water, and of a possible concentrated alkali addition. To be able to apply the above selected CVs (technically and economically feasible to measure/estimate), a simplified tailored AD model was specifically designed as the prediction model, integral part of the NMPC system. The NMPC system developed was evaluated for a case scenario consisting of the automatic start-up of a high rate AD reactor treating a readily biodegradable carbohydrate based substrate. The AD plant was virtually represented by the complex Anaerobic Digestion Model No. 1. Compared to other manual start-up strategies, the two configurations of the NMPC developed appeared to reach the target methane production rate faster (39 and 18 days for the NMPC versus 70-75 days for the manual strategies) together with an overall superior CV set-point tracking error performance. Interestingly, the two configurations of the NMPC developed appear to propose two very different, almost opposite, start-up feeding strategies to both eventually start-up the reactor successfully with no process destabilizations throughout. A number of practical scenarios were also considered to evaluate the NMPC configurations for robustness and any possible improvements. These tests indicate that the NMPC objective function formulation is a key factor of the success and robustness exhibited during start-up.
多年来，以尽可能低的成本（包括过程监测成本）实现厌氧消化器的最佳自动启动一直是一个难以解决的问题。在这项工作中，根据两种提出的配置，为处理可溶性非抗性基质的厌氧消化器的最佳启动开发了一种非线性模型预测控制（NMPC）系统。为工艺启动选择的最低成本实用控制变量（CV）集包括：（i）出水质量（以 COD 计）、（ii）反应器中乙酸营养型产甲烷菌的生物量水平，以及（iii）甲烷产率（仅适用于 NMPC 配置之一）。操作变量（MV）包括有机底物、稀释水的体积流入率以及可能的浓缩碱添加量。为了能够应用上述选定的 CV（在技术和经济上可行的测量/估计），专门设计了简化的定制 AD 模型作为预测模型，这是 NMPC 系统的组成部分。开发的 NMPC 系统针对一个高负荷 AD 反应器自动启动的案例进行了评估，该反应器处理可生物降解的碳水化合物基底物。AD 工厂实际上由复杂的厌氧消化模型 1 代表。与其他手动启动策略相比，所开发的 NMPC 的两种配置似乎更快地达到目标甲烷产率（NMPC 为 39 天和 18 天，而手动策略为 70-75 天），同时具有整体更好的 CV 设定点跟踪误差性能。有趣的是，所开发的 NMPC 的两种配置似乎提出了两种非常不同的、几乎相反的启动进料策略，最终成功启动了反应器，整个过程没有出现不稳定。还考虑了一些实际情况来评估 NMPC 配置的鲁棒性和任何可能的改进。这些测试表明，NMPC 目标函数的制定是启动过程中成功和鲁棒性的关键因素。
