In this paper, a general strategy is presented for optimizing water resources in industrial activities taking into account fluctuations of water flow-streams and water contents in feed and products as well as in the utility system. Indeed, considering fluctuations can be of primary importance for several water intensive industries such as the food or the paper&pulp industry. Furthermore, whenever the water balance includes the water content of products (as is typically the case for the food industry) fluctuations in the composition of products can considerably affect the overall balance due to water being a major component of many foods. Therefore, the currently used optimization strategies that disregard water fluctuations, can lead to a severe bias in the water balance and thus overlook the potential of water recovery and reuse.

Including this aspect in widely used algorithms for process monitoring (data reconciliation) and optimization might lead to a possibly prohibitive increase in the computational burden. In particular, it is shown in this paper that the resulting optimization algorithm should solve a dynamic non-convex stochastic Mixed Integer Nonlinear Programming problem.

To tackle this (typically NP-hard) problem, a strategy has been developed that combines ordinal optimization for dealing with statistical information and nonlinear, non-convex deterministic algorithms.

The outlined strategy has been applied to a complex process in the food industry (production of starch and starch-based products). The results seem to confirm the general validity of the algorithm developed.

在本文中，提出了一种在工业活动中优化水资源的通用策略，该策略考虑了水流的波动以及饲料，产品以及公用事业系统中的水分含量。的确，考虑波动对几个用水密集型行业（例如食品或造纸业）至关重要。此外，只要水平衡包括产品中的水分（通常是食品工业的情况），由于水是许多食品的主要成分，产品成分的波动会极大地影响总体平衡。因此，当前使用的忽略水波动的优化策略会导致水平衡严重偏差，从而忽略了水回收和再利用的潜力。

在广泛用于过程监控（数据协调）和优化的算法中包括此方面，可能会导致计算负担的增加。特别是，本文表明，所得的优化算法应能解决动态非凸随机混合整数非线性规划问题。

为了解决此问题（通常是NP难题），已开发出一种策略，该策略结合了用于处理统计信息的有序优化和非线性，非凸确定性算法。

概述的策略已应用于食品工业的复杂过程（生产淀粉和淀粉基产品）。结果似乎证实了所开发算法的一般有效性。