One of the main problems in water management of irrigation systems is the control of the equitable distribution of water among different orifice offtakes. The difficulty of managing a canal is partly caused by the lack of knowledge of the canal state because the scheduled demand is often not fulfilled, since farmers extract more water than is scheduled and it is impossible for the watermaster to determine the canal state. However, an innovative developed algorithm called CSE is proposed in this paper. This algorithm is able to estimate the real extracted flow and the hydrodynamic canal state (that is, the water level and velocity along the irrigation canal). The algorithm solves an inverse problem implemented as a nonlinear optimization problem using the Levenberg–Marquardt method. The algorithm is tested, taking into account several numerical examples, and a practical implementation is made for a real case study in the PAC-UPC canal, a 220 m laboratory canal especially designed for research into irrigation canal control area and irrigation canal modelling. This useful algorithm evaluates the real extraction flow and the canal state and could be a useful tool for a feedback controller.

Abstract:The agriculture holds an important part of the food chain and the water resources for agriculture are essential. The problem is the water transport systems present low efficiencies in practice. The yield agriculture has to be optimized, because the goal of an operational water manager is to deliver the water to the irrigation sites accurately and efficiently. In order to fulfill this objective, we propose a centralized overall control diagram to optimize the management of the canal. Our control diagram in real-time is mainly composed by two algorithms, CSE and GoRoSoBo. The first one is a powerful tool in canal management, so that it is able to estimate the real extracted flow in the canal and the hydrodynamic canal state from measured level data at selected points. The second one is an essential tool in the management of a canal, a feedback control algorithm operating in real-time. The GoRoSoBo algorithm (Gómez, Rodellar, Soler, Bonet) is able to calculate the optimum gates trajectories for a predictive horizon taking into account the current canal state and the real extracted flow both obtained by CSE. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems CorporationTitle: GOROSOBO: An overall control diagram to improve the efficiency of water transport systems in real time Manuscript Click here to download Manuscript PaperGRSB_17_10_2016. doc 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 ABSTRACTThe agriculture holds an important part of the food chain and the water resources for agriculture are essential. The problem is the water transport systems present low efficiencies in practice. The yield agriculture has to be optimized, because the goal of an operational water manager is to deliver the water to the irrigation sites accurately and efficiently. In order to fulfill this objective, we propose a centralized overall control diagram to optimize the management of the canal. Our control diagram in real-time is mainly composed by two algorithms, CSE and GoRoSoBo. The first one is a powerful tool in canal management, so that it is able to estimate the real extracted flow in the canal and the hydrodynamic canal state from measured level data at selected points. The second one is an essential tool in the management of a canal, a feedback control algorithm operating in real-time. The GoRoSoBo algorithm (Gómez, Rodellar, Soler, Bonet) is able to calculate the optimum gates trajectories for a predictive horizon taking into account the current canal state and the real extracted flow both obtained by CSE.

Modeling solute transport in heterogeneous porous media faces two challenges: scale dependence of dispersion and reproducing mixing separately from spreading. Both are crucial since real applications may require km scales whereas reactions, often controlled by mixing, may occur at the pore scale. Methods have been developed in response to these challenges, but none has satisfactorily characterized both processes. In this paper, we propose a formulation based on the Water Mixing Approach extended to account for velocity variability. Velocity is taken as an independent variable, so that concentration depends on time, space and velocity. Therefore, we term the formulation the Multi-Advective Water Mixing Approach. A new mixing term between velocity classes emerges in this formulation. We test it on Poiseuille’s stratified flow using the Water Parcel method. Results show high accuracy of the formulation in both dispersion and mixing. Moreover, the mixing process exhibits Markovianity in space even though it is modeled in time.