An optimal resource recovery of biogas, water regeneration, and reuse network integrating domestic and industrial sources

Estimating and forecasting suspended sediments concentrations in streams constitutes a valuable asset for sustainable land management. This research presents the development of a non-linear autoregressive exogenous neural network (NARX) for forecasting sediment concentrations at the exit of Francia Creek watershed (Valparaiso, Chile). Details are presented on input data selection, data splitting, selection of model architecture, determination of model structure, NARX training (optimization of model parameters), and model validation (hindcasting and forecasting). The study explored if the developed artificial neural network model is valid for forecasting daily suspended sediment concentrations for a complete year, capturing seasonal trends, and maximum and baseflow concentrations. Francia Creek watershed covers approximately 3.24 km2. Land cover within the catchment consists mainly of native and exotic vegetation, eroded soil, and urban areas. Input data consisting of precipitation and stream flow time-series were fed to a NARX network for forecasting daily suspended sediments (SST) concentrations for years 2013–2014, and hindcasting for years 2008–2010. Training of the network was performed with daily SST, precipitation, and flow data from years 2012 and 2013. The resulting NARX net consisted of an open-loop, 12-node hidden layer, 100 iterations, using Bayesian regularization backpropagation. Hindcasting of daily and monthly SST concentrations for years 2008 through 2010 was successful. Daily SST concentrations for years 2013 and 2014 were forecasted successfully for baseflow conditions (R2 = 0.73, NS = 0.71, and Kling-Gupta efficiency index (K-G) = 0.84). Forecasting daily SST concentrations for year 2014 was within acceptable statistical fit and error margins (R2 = 0.53, NS = 0.47, K-G = 0.60, d = 0.82). Forecasting of monthly maximum SST concentrations for the two-year period (2013 and 2014) was also successful (R2 = 0.69, NS = 0.60, K-G = 0.54, d = 0.84).

https://www.mdpi.com/2071-1050/13/1/363/pdf

Hindcasting and Forecasting Total Suspended Sediment Concentrations Using a NARX Neural Network

Industrial processes provide several of the products and services required for society. However, each industry faces different challenges from different perspectives, all of which must be reconciled to obtain profitable, productive, controllable, safe and sustainable processes. In this context, multi-objective optimization has become a powerful tool to aid the decision-making mechanism in the synthesis, design, operation and control of such processes. The solution to the mathematical models provides the necessary tools to asses the system performance in terms of different metrics and evaluate the trade-offs between the objectives in conflict. The number of applications of multi- objective optimization in industrial processes is ample and each application has its own challenges. In the present literature review, a broad panorama of the applications in multi-objective optimization is presented, including future perspectives and open questions that still need to be addressed. 

/pdf/applications-of-multi-objective-optimization-to-industrial-286ibbrn.pdf

Applications of Multi-Objective Optimization to Industrial Processes: A Literature Review

Water resource management is a crucial issue today when global warming is advancing, generating water shortages in several world countries. Mathematical and optimization tools have addressed these problems, including new alternative water sources. Water networks are designed to cover consumption. In this context, pumping is critical in modeling water networks through optimization techniques because of the nonlinear terms from the extended Bernoulli equation with the Darcy–Weisbach friction term. This paper compares four strategies to simplify these terms, focusing on the nonlinear constraints generated by the extended Bernoulli equation with the Darcy–Weisbach friction term. This equation has a significant impact on operation costs because of pumping power. The four simplification strategies were compared with a focus on (i) solution, (ii) error, and (iii) execution time. The best results were obtained with a two-stage strategy to address large MINLP multiobjective problems. This strategy is applied to a model with three objective functions (freshwater inlet, global warming potential, and total cost) to illustrate the simplification performance in a city-scale water network. The problem is focused on a case study in Santiago, Chile, and is based on a previous formulation. By solving the multiobjective problem, some results and changes in the network are obtained. When the three objective functions have all the same importance, the results show the following: (i) The current location of water treatment plants is suboptimal. (ii) Water recycling in the city is the best option, with drinking and irrigation qualities. (iii) With the optimal configuration, Santiago can reduce their water consumption by 30%, increasing the economic cost by 108% and the global warming potential by 49%. Finally, this model can be implemented in other contexts to approach nonlinearities by the extended Bernoulli equation with the Darcy–Weisbach friction term in large-scale water networks. 

Modeling Frictional Head Loss in the Redesign of City-Scale Water Networks through Multiobjective Optimization: Strategies to Deal with Nonlinear Terms

In the simplex method of linear programming,there is a big M method(the penalty factor method) for finding an initial feasible basis.The current textbooks of operations research only explain that the big M method is efficient when M is large enough,and never give precise evaluation to the parameter M.This paper determines a constant M0 and proves that the big M method is convergent to an optimal solution of the primal problem when MM0.

The Evaluation of Parameter M in the Big M Method of Linear Programming

https://hal.archives-ouvertes.fr/hal-03159940/document

Javiera Riffo-Rivas

Papers

A multi-objective optimization model to plan city-scale water systems with economic and environmental objectives: a case study in Santiago, Chile