Luigi Berardi

Efficient multi-objective optimal design of water distribution networks on a budget of simulations using hybrid algorithms

The design of water distribution networks is a large-scale combinatorial, non-linear optimisation problem, involving many complex implicit constraint sets, such as nodal mass balance and energy conservation, which are commonly satisfied through the use of hydraulic network solvers. These problem properties have motivated several prior studies to use stochastic search optimisation, because these derivative-free global search algorithms have been shown to obtain higher quality solutions for large network design problems. Global stochastic search methods, however, require many iterations to be performed in order to achieve a satisfactory solution, and each iteration may involve running computationally expensive simulations. Recently, this problem has been compounded by the evident need to embrace more than a single measure of performance into the design process, since by nature multi-objective optimisation methods require even more iterations. The use of metamodels as surrogates for the expensive simulation functions has been investigated as a possible remedy to this problem. However, the identification of reliable surrogates is not always a viable alternative. Under these circumstances, methods that are capable of achieving a satisfactory level of performance with a limited number of function evaluations represent a valuable alternative. This paper represents a first step towards filling this gap. Two recently introduced multi-objective, hybrid algorithms, ParEGO and LEMMO, are tested on the design problem of a real medium-size network in Southern Italy, and a real large-size network in the UK under a scenario of a severely restricted number of function evaluations. The results obtained suggest that the use of both algorithms, in particular LEMMO, could be successfully extended to the efficient design of large-scale water distribution networks.

Operational and Tactical Management of Water and Energy Resources in Pressurized Systems: Competition at WDSA 2014

AbstractOptimal management of water and energy resources worldwide is a basis for environmental and socioeconomic sustainability in urban areas, which has become even more relevant with the advent of the smart and water sensitive city paradigm. In water distribution networks (WDNs) water resource management is concerned with increased efficiency, which is primarily related to the reduction of leakages, whereas energy management refers to optimal pump, valve, and source scheduling strategies considering the hydraulic system requirements. These management goals require planning of asset renewal and improvement works in the short time (operational) and medium time (tactical) horizons, considering the financial sustainability of relevant actions. The battle of background leakage assessment for water networks (BBLAWN) was designed as a competition held at the 16th Water Distribution Systems Analysis Conference, in Bari (Italy) in 2014 (WDSA), to address the aforementioned management goals. The teams taking par...

Computationally Efficient Modeling Method for Large Water Network Analysis

AbstractNowadays, the unprecedented computing power of desktop personal computers and efficient computational methodologies such as the global gradient algorithm (GGA) make large water-distribution-system modeling feasible. However, many network analysis applications, such as optimization models, require running numerous hydraulic simulations with modified input parameters. Therefore, a methodology that can reduce the computational burden of network analysis and still provide the required model accuracy is needed. This paper presents a matrix transformation approach to convert the classic GGA, which is implemented within the widely available freeware EPANET 2, into a more computationally efficient enhanced global gradient algorithm (EGGA). The latter achieves improved efficiency by reducing the size of the mathematical problem through the transformed topological representation of the original network model. By removing serial nodes and serial pipe sections from the original topological representation whil...

Operational Optimization: Water Losses versus Energy Costs

Management efficiency of water distribution networks (WDNs) is of relevant interest for the water industry, and operational optimization plays an important role. The energy to pump water is a significant element of operational costs and depends on electricity tariffs varying over time. As a result, pumping optimization accounting for electricity costs and relevant boundary conditions of a WDN, e.g., demands, is of practical interest. When the electricity tariffs are lower, for example, during the night hours, optimization generally results in pumping more water during those hours, if the presence of tanks, which are internal to the hydraulic system, allows for water storage. Nevertheless, the pressure and therefore, water leakage of the network greatly vary from night to daylight hours. Pressure and leakage generally increase in the night because of a lower level of demands and a greater level of pressures. Previous studies rarely account for this. This work investigates pumping optimization background leaks, i.e., the nonrevenue water cost beside the energy cost. It is shown and discussed that the reduction of background leaks conflict with, and generally dominate, energy cost. DOI: 10.1061/(ASCE)HY.1943- 7900.0000681.

An effective multi-objective approach to prioritisation of sewer pipe inspection

The first step in the decision making process for proactive sewer rehabilitation is to assess the condition of conduits. In a risk-based decision context the set of sewers to be inspected first should be identified based on the trade-off between the risk of failures and the cost of inspections. In this paper the most effective inspection works are obtained by solving a multi-objective optimization problem where the total cost of the survey programme and the expected cost of emergency repairs subsequent to blockages and collapses are considered simultaneously. A multi-objective genetic algorithm (MOGA) is used to identify a set of Pareto-optimal inspection programmes. Regardless of the proven effectiveness of the genetic-algorithm approach, the scrutiny of MOGA-based inspection strategies shows that they can differ significantly from each other, even when having comparable costs. A post-processing of MOGA solutions is proposed herein, which allows priority to be assigned to each survey intervention. Results are of practical relevance for decision makers, as they represent the most effective sequence of inspection works to be carried out based on the available funds. The proposed approach is demonstrated on a real large sewer system in the UK.

Assessing climate change and asset deterioration impacts on water distribution networks: Demand-driven or pressure-driven network modeling?

This manuscript compares demand-driven and pressure-driven hydraulic network simulation models for assessing hydraulic capacity under uncertain scenarios. A stochastic approach is implemented assuming possible alteration of boundary conditions due to climate and socio-economic changes (i.e., the increase of peaks of customers demands), and system deterioration (i.e., the increase of pipe internal hydraulic resistances and background leakages). Two real water distribution networks located in Southern Italy are used for analyses. Results show that demand-driven analysis underestimates the hydraulic network capacity with respect to pressure-driven analysis. In fact, pressure-driven analysis assumes the components of model demands (human-based and leakage-based) as dependent on pressure status of the system, and thus returns a more reasonable number and location of critical nodes than demand-driven analysis. Furthermore, demand-driven analysis does not predict the water demand that can be realistically supplied to customers under pressure-deficient system functioning. Therefore, the use of pressure-driven analysis is advisable to support water managers to allocate budgets for planning rehabilitation works aimed at increasing the hydraulic capacity of the networks.

Predicting torsional strength of RC beams by using Evolutionary Polynomial Regression

A new view for the analytical formulation of torsional ultimate strength for reinforced concrete (RC) beams by experimental data is explored by using a new hybrid regression method termed Evolutionary Polynomial Regression (EPR). In the case of torsion in RC elements, the poor assumptions in physical models often result into poor agreement with experimental results. Nonetheless, existing models have simple and compact mathematical expressions since they are used by practitioners as building codes provisions. EPR combines the best features of conventional numerical regression techniques with the effectiveness of genetic programming for constructing symbolic expressions of regression models. The EPR modeling paradigm allows to figure out existing patterns in recorded data in terms of compact mathematical expressions, according to the available physical knowledge on the phenomenon (if any). The procedure output is represented by different formulae to predict torsional strength of RC beam. The multi-objective search paradigm used by EPR allows developing a set of formulae showing different complexity of mathematical expressions as resulting into different agreement with experimental data. The efficiency of such approach is tested using experimental data of 64 rectangular RC beams reported in technical literature. The input parameters affecting the torsional strength were selected as cross-sectional area of beams, cross-sectional area of one-leg of closed stirrup, spacing of stirrups, area of longitudinal reinforcement, yield strength of stirrup and longitudinal reinforcement, concrete compressive strength. Those results are finally compared with previous studies and existing building codes for a complete comparison considering formulation complexity and experimental data fitting.

Assessing mechanical vulnerability in water distribution networks under multiple failures

Understanding mechanical vulnerability of water distribution networks (WDN) is of direct relevance for water utilities since it entails two different purposes. On the one hand, it might support the identification of severe failure scenarios due to external causes (e.g., natural or intentional events) which result into the most critical consequences on WDN supply capacity. On the other hand, it aims at figure out the WDN portions which are more prone to be affected by asset disruptions. The complexity of such analysis stems from the number of possible scenarios with single and multiple simultaneous shutdowns of asset elements leading to modifications of network topology and insufficient water supply to customers. In this work, the search for the most disruptive combinations of multiple asset failure events is formulated and solved as a multiobjective optimization problem. The higher vulnerability failure scenarios are detected as those causing the lower supplied demand due to the lower number of simultaneous failures. The automatic detection of WDN topology, subsequent to the detachments of failed elements, is combined with pressure-driven analysis. The methodology is demonstrated on a real water distribution network. Results show that, besides the failures causing the detachment of reservoirs, tanks, or pumps, there are other different topological modifications which may cause severe WDN service disruptions. Such information is of direct relevance to support planning asset enhancement works and improve the preparedness to extreme events.

Modeling Local Water Storages Delivering Customer Demands in WDN Models

Water distribution network (WDN) models account for customer-demands as water withdrawals concentrated in nodes. Customer- demands can be assumed to be constant or varying with nodal head/pressure entailing demand-driven or pressure-driven simulation, respec- tively. In both cases, the direct connection of customer properties to the hydraulic system is implicitly assumed. Nonetheless, in many technical situations, the service pipe fills a local private storage (e.g., a roof tank or a basement tank) from which the water is actually delivered to customers by gravity or pumping systems. In such contexts, the service pipe fills the local tank by means of a top orifice. Consequently, what is really connected to the hydraulic system is a tank, which is subject to a filling/emptying process while supplying water to customers. Therefore, since modeling this technical situation in WDN analyses is necessary, the paper develops a formulation for nodal water withdrawals in WDN models accounting for the filling/emptying process of inline tanks between the hydraulic network and customers. The formulation is also introduced in a widely used method for steady-state WDN modeling, the global gradient algorithm, and its effectiveness to increase the hydraulic accuracy of results is discussed using a simple case study and a small network. DOI: 10.1061/(ASCE) HY.1943-7900.0000812.

Accounting for uniformly distributed pipe demand in WDN analysis: enhanced GGA

The global gradient algorithm (GGA) is the most widely adopted method for steady-state analysis of water distribution networks. It is used to solve the non-linear system of equations describing mass and energy conservation laws. Nonetheless, it has been recently proved that the usually adopted representation of distributed pipe demands as lumped withdrawals at ending nodes causes inconsistent calibration results and pipe head loss errors which could be non negligible in some network conditions. The original GGA has been contextually modified by introducing a correction of pipe hydraulic resistance under the assumptions of a friction factor independent from the flow regime. This paper aims at providing researchers and software developers with a general formulation of the GGA which entails both the adoption of any generic monomial head loss formula and pipe hydraulic resistance dependence on flow regime. The results could be easily extended to other methods of network analysis.