Project Title: Improvement of energy efficiency in ... · gamification for online self-assessment,...

H2020-EE-2015-3-MarketUptake

Project Title: Improvement of energy efficiency in industrial water circuits using gamification for online self-assessment, benchmarking and economic decision support Acronym: WaterWatt Grant Agreement No: 695820

Deliverable No. D2.1.

Deliverable Title Report with the description of the methodology used to modelling the

typical water circuits.

Due Date 30/06/2017

Date Delivered 30/06/2017

Prepared by (Lead Partner) ISQ

Dissemination Level Public

WaterWatt - Del.No.D2.1 WATERWATT 695820 30 JUNE 2017

WaterWatt Public

Table of Content

1. Executive Summary .............................................................................................................. 3

2. Objectives of the Work Package 2 and the specific objectives of the Task 2.1. and

the objectives of this report..................................................................................................... 3

3. Introduction ........................................................................................................................... 3

4. Purpose of the models ......................................................................................................... 4

5. Programming language used to assembly the models: Modelica ................................. 5

6. Why Modelica? ...................................................................................................................... 6

7. Edition Tool: OMEdit ............................................................................................................ 7

8. Libraries ................................................................................................................................. 8

9. Step by step guide to the model creation .......................................................................... 8

10. Examples of WaterWatt models ...................................................................................... 13

11. Conclusion ......................................................................................................................... 16

12. References ......................................................................................................................... 16


WaterWatt Public

1. Executive Summary

This report is the first deliverable of the Work Package (WP) 2, which is the task of modelling and

optimization of the selected industrial water circuits, that constitute the ten case studies of this

project. This WP has the objective of generate relevant information from the case studies,

performing its physical modelling (of the circuits and their components) and defining the best Key

Performance Indicators (KPI) for assess the energy efficiency performance of both components

and circuits. These chosen KPI (delivered in document D2.2.) will allow the evaluation of the

performance of typified water circuits and to simulate the impact of the implementation of the

available energy efficiency solutions.

With the completion of WP2 it will be also possible identify the improvement energy efficiency

potential of each circuit and compare it with other similar circuits from through a benchmark

analysis.

The specific objectives of Task 2.1. is to physically model the ten selected case study circuits,

selected in WP1, and provide generic models of each component of the ten studied circuits.

This report overviews the process of producing the models that simulates the industrial water

circuits from the WaterWatt case studies.

Though the report will be explained the methodology followed and the choices taken through the

process of producing the WaterWatt models.

2. Objectives of the Work Package 2 and the specific objectives of the

Task 2.1. and the objectives of this report.

This report (D2.1.) has the objective to explain the methodology that was followed to model the

different types of industrial water circuit from our case studies.

3. Introduction

Industrial water circuits are large energy consumers, mainly due to pumping and other continuous

operations such as water cooling and gas or water treatment. For an efficient operation of these

circuits, it is necessary to know how much energy is being consumed and how efficient its use is.

Water circuits are widely used in almost all, if not every, industrial sector, providing transportation

and services of cooling and heating for the other industrial systems, as well as in many other

industrial applications. From the water circuits, pumping systems alone account for nearly 20% of

the world’s electrical energy demand and range from 25-50% of the energy usage in certain

industrial plant operations (Hydraulic Institute, 2001). Therefore, the efficiency of these circuits is

of the utmost importance for the companies and for the nations in general.

However, a significant part of the energy that is consumed by water circuits is associated to the

inefficiency of the equipment or poor project. Equipment performance is also expected to

decrease after a long and heavy duty service life as a result of erosion, corrosion, wear and

cavitation. As an example, much of a water pump wear occurs in the first years of operation and


WaterWatt Public

tends to level out after the first ten years. The overall drop in the efficiency for an inadequately

maintained pump can be 5% in the first five years of operation and up to 15% in the first ten years

(IMechE, 1989). Part of this loss of efficiency is due to degradation of the impeller/casing wear

rings. The degradation rate tends to be much higher in pumps operating far from the best-

efficiency point due to increased shaft deflection. Most of the loss in efficiency is normally caused

by a build-up of corrosion products in cast iron casing. This occurs in practically all pumps working

with clean cold water. Therefore, periodically maintaining the pump can return its efficiency to a

similar level of a new pump (UK Department of the Environment, 1998).

As demonstrated in the previous example, equipment inefficiencies lead to direct economic

impacts in operating costs of the industries, due to higher energy consumption, and high utilization

factor of these circuits. That is why it is crucial to know how efficient are our circuits and how they

use energy. In order to answer these questions for our ten case studies it was necessary to assess

energy audits for each circuit and to build its physical model.

The term “physical model” refers to the behaviour of physical-base models found across many

disciplines (e.g., electrical engineering, mechanical engineering, chemistry and physics). Such

systems can be identified by their use of conservation principals. An example of a typified

industrial water circuit is a pumping circuit. The main purpose of this circuit is to pump a certain

volume of a fluid from one place to another, by the difference of pressure observed in the different

parts of the circuits. In respect to the energy consumption there are other variables that influence

the energy consumption such as flow rate, pipe length, pipe diameter, pressure drop on the

filtration unit, pressure drop on heat exchanger, heat exchanger efficiency, cooling tower

efficiency, fan efficiency, pump efficiency and electric motor efficiency. All these identified

parameters must be introduced in the physical model in order to define the energy consumption

behaviour of each component of the typical industrial water circuits.

The ten case studies of the project have the following share of typical industrial water circuits, as

showed in Table 1.

Table 1: Distribution of the typical circuits, per industrial sector

Industry Sector Type of Representative Circuit

Chemical, Food and Beverage, Metal Open cooling water circuit

Metal Closed cooling water circuit

Metal Open gas washing circuit

Metal Open cooling/quenching water circuit

Paper Raw material transportation circuit

Food and Beverage Water treatment circuit

4. Purpose of the models

The models that are produced, for each industrial circuit, will have three use cases.

Use case 1 – Simulation of the real case studies.

The aim of this use case is to simulate and calibrate the physical behaviour of the real case

studies.


WaterWatt Public

The objective of the calibration is to make sure that our model replicates the performance of the

simulate case study. In order to verify such assumption, our model output must reflect, as closely

as possible, the result of actual operational data of the circuits collected previously from each

case study, in WP1.

This use case will have time dependent variables (namely water demand, and number of

operating hours per pump, variation of motors frequency over the time). This case will be used

only by WaterWatt partners, to produce results for the validation of the models.

This use case is very complex for inexpedient users because the variables (inputs) that the model

can receive, are not limited and the inputs are not coherent with the correct initialization of the

model, errors would certainly happen. If we provided these infinite possibilities to the inexpedient

users, this amount of freedom will for sure transformed into frustration and this will possible lead

to giving up the gamification experience.

Use case 2 – Industrial Self-Assessment.

The aim of this use case is to allow the industrial users to assess the overall energy efficiency of

their circuit (if it has a similar configuration as one of the proposed case studies) and the efficiency

of the circuit component (for example pump, fans, cooling tower, heat exchanger, filters, etc.).

This use case will be to be part of the WaterWatt project platform from WP4, but with restricted

number of inputs for the model. The output of this model will be the generation of KPI for the

assessment of the energy efficiency of the global circuit and also per component present in the

circuit. These KPI are defined in D2.2 and their purpose is to be compared with the same KPI

from the best efficient circuits and equipment with similar operational conditions and capabilities

for the same industrial sector, in order to be performed a benchmark analysis, per component

circuit and industrial sector.

Use case 3 – Simulation of improvement actions.

The aim of this use case is to allow the industrial users to simulate some prescribed actions to

improve the efficiency of their circuit (with similar configuration as the one from use case). Some

examples of these prescribed actions are: change the efficiency of the components, operate the

pumps with lower frequency and higher operating hours, change of pressure drop of some

components, etc. This use case will be to be also part of the WaterWatt project platform. The

objective of these use case is to provide ten typified water circuits models that can be used in a

controlled way to permit a sell assessment energy performance of a similar circuit and the

simulation of other operational conditions of that circuits to achieve better energy efficient

operational performances.

5. Programming language used to assembly the models: Modelica

The chosen programming language to produce the models is Modelica (version 3.2). Modelica is

an Open Source language for modelling of physical systems, designed to support effective library

development and model exchange. It is a modern language built on acausal modelling with

mathematical equations and object-oriented constructs to facilitate reuse of modelling knowledge.


WaterWatt Public

The semantics of the Modelica language is specified by means of a set of rules for translating any

class described in the Modelica language to a flat Modelica structure. A class must have additional

properties in order that its flat Modelica structure can be further transformed into a set of

differential, algebraic and discrete equations. Such classes are called simulation models.

The flat Modelica structure is also defined for other cases than simulation models; including

functions (can be used to provide algorithmic contents), packages (used as a structuring

mechanism), and partial models (used as base-models). This allows correctness to be verified

before building the simulation model.

Modelica was designed to facilitate symbolic transformations of models, especially by mapping

basically every Modelica language construct to continuous or instantaneous equations in the flat

Modelica structure. Many Modelica models, especially in the associated Modelica Standard

Library, are higher index systems, and can only be reasonably simulated if symbolic index

reduction is performed, i.e., equations are differentiated and appropriate variables are selected

as states, so that the resulting system of equations can be transformed to state space form (at

least locally numerically). The Modelica specification does not define how to simulate a model.

However, it defines a set of equations that the simulation result should satisfy as well as possible.

6. Why Modelica?

Modelica is a freely available, object-oriented language for modelling of large, complex, and

heterogeneous physical systems. From a user’s point of view, models are described by

schematics, also called object diagrams. Examples are shown in the next figure.

Figure 1 – Examples of Modelica models (source Modelica Language Specification)

It is very important that the user could have a graphic visualization of the models otherwise it

could be difficult to have their attention. This graphic visualization is called a schematic and it


WaterWatt Public

consists of connected components, like pump, or a deposit. A component has “connectors” (often

also called “ports”) that describe the interaction possibilities, e.g., a electric motor and the shaft

of a pump, or an input signal. By drawing connection lines between connectors a physical system

or block diagram model is constructed. Internally a component is defined by another schematic

or on “bottom” level, by an equation based description of the model in Modelica syntax.

The Modelica language is a textual description to define all parts of a model and to structure

model components in libraries, called packages. An appropriate Modelica simulation environment

is needed to graphically edit and browse a Modelica model (by interpreting the information

defining a Modelica model) and to perform model simulations and other analysis. Basically, all

Modelica language elements are mapped to differential, algebraic and discrete equations. There

are no language elements to describe directly partial differential equations, although some types

of discretized partial differential equations can be reasonably defined, e.g., based on the finite

volume method and there are Modelica libraries to import results of finite-element programs.

The main advantages of Modelica, compared to other languages is due to it is a freely available

and suited for modelling of large, complex, and heterogeneous physical systems. It is suited for

multi‐domain modelling, for example, hydraulic and thermal models. Also, Modelica is designed

such that it can be utilized in a similar way as an engineer builds a real system: First trying to find

standard components like motors, pumps and valves from manufacturers catalogues with

appropriate specifications and interfaces and only if there does not exist a particular subsystem,

a component model would be newly constructed based on standardized interfaces.

7. Edition Tool: OMEdit

The models are assembled using the OpenModelica Connection Editor OMEdit. OMEdit is an

advanced open source user friendly graphical user interface that provides the users with easy‐to‐

use model creation, connection editing, simulation of models, and plotting of results. The interface

is extensible enough to support user‐defined extensions/models. Models can be both textual and

graphical.

The main reason behind the chosen of this editor was the fact that it is open-source and also it is

a user friendly, efficient and modular OpenModelica graphical connection editor. There are other

Modelica open source editors but they are either only textual or not so user friendly.

OMEdit provides user friendly features like:

Modeling – Easy Modelica model creation;

Pre‐defined models – Browsing the Modelica Standard Library to access the provided

models;

User defined models – Users can create their own models for immediate usage and later

refinement and reuse;

Component interfaces – Smart connection editing for drawing and editing connections

between model interfaces;


WaterWatt Public

Simulation subsystem – Subsystem for running simulations and specifying simulation

parameters start and stop time, etc.

Plotting – Interface to plot variables from simulated models.

8. Libraries

The models will use only open source libraries from the Modelica Standard Library and also

thermoPower open-source library.

The Modelica Standard Library is a free library that can be used without essential restrictions. It

defines the most important elementary connectors in various domains. If any possible, a user

should utilize these connectors in order that components from the Modelica Standard Library and

from other libraries can be combined without problems. From the Modelica Standard Library the

sub-libraries that will be mostly used are the Modelica.Fluid library which has components for 1-

dimensional thermo-fluid flow in networks of vessels, pipes, fluid machines, valves and fittings.

All components of this sub library are implemented such that they can be used for media from the

Modelica.Media library.

The other important library used is ThermoPower. The ThermoPower library is an open-

source Modelica library for the dynamic modelling of thermal power plants and energy conversion

systems. It provides basic components for system-level modelling, in particular for the study of

control systems in traditional and innovative power plants and energy conversion systems.

9. Step by step guide to the model creation

This chapter gives a brief introduction to the process of producing a model on OMEdit, by

analysing the creation of a very simple model of water transfer form one tank to other, by gravity,

using the Modelica Standard Library. Although the users of the platform (of WP4) of WaterWat

project will never have to produce this kind of models, because the models that the WaterWatt

consortium will deliver are already made and do not required any knowledge of Modelica. This

chapter is just to briefly illustrate the major steps of producing a model in OMEdit.

Problem definition.

To transfer water from tank 1 which is 5 meters above tank 2, into the last. The two tanks are

connected by a 30cm diameter tube. The water outlet of tank 1 is placed at the bottom, the inlet

of tank 2 is placed 50cm above the bottom. Both tanks have a cross area of 2m2 and a high of

10m. At the time=0, the water level in tank1 is 9m from its bottom, tank 2 is empty.

Creating a new model file.

Creating a new file/model in OMEdit is rather straightforward. In OMEdit the new model can be

of type model, class, connector, record, block, function or package.

http://www.modelica.org/libraries


WaterWatt Public

Adding components and making connections.

The Modelica standard library is loaded automatically. Instances of component models available

in the Modelica Standard Library can be added to the currently edited model by doing a drag and

drop from the Library Window. The user has to navigate to the component model in the library

tree, click on it, drag it to the model he is building

And if the user is lucky enough to find all components needed form the standard and already

loaded libraries, he just have to drag and drop the components needed and join the connectors.

In order to connect one component model to another the user simply clicks on any of the ports.

Then it will start displaying a connection line. Then move the mouse to the target component

where the user want to finish the connection and click on the component port where the

connection should end. If the component is not found in the standard libraries, the user has to

upload other libraries first, or build the components by scratch, which is much more complex and

time consuming.

The user should arrive this step with a scheme similar with Figure 2.

Figure 2 – Scheme produced by drag and drop and connecting components.

Alternatively the user can produce Modelica code. The transcription of the above scheme has the

following into code has the following aspect.

model Two_tanks_transfer

extends Modelica.Icons.Example;


WaterWatt Public

Modelica.Fluid.Vessels.OpenTank tank1(redeclare package Medium =

Modelica.Media.Water.ConstantPropertyLiquidWater, crossArea = 2, height = 10,

level_start = 9, nPorts = 1, portsData =

{Modelica.Fluid.Vessels.BaseClasses.VesselPortsData(diameter = 0.3)})

annotation(Placement(visible = true, transformation(origin = {0, 54}, extent = {{-20,

-20}, {20, 20}}, rotation = 0)));

Modelica.Fluid.Pipes.StaticPipe pipe(redeclare package Medium =

Modelica.Media.Water.ConstantPropertyLiquidWater, diameter = 0.3, height_ab = -5,

length = 5) annotation(Placement(visible = true, transformation(origin = {0, 0},

extent = {{-10, -10}, {10, 10}}, rotation = -90)));

Modelica.Fluid.Vessels.OpenTank tank2(redeclare package Medium =

Modelica.Media.Water.ConstantPropertyLiquidWater, portsData =

{Modelica.Fluid.Vessels.BaseClasses.VesselPortsData(diameter = 0.1, height = 0.5)},

crossArea = 2, height = 10, level_start = 0, nPorts = 1) annotation(Placement(visible

= true, transformation(origin = {40, -40}, extent = {{-20, -20}, {20, 20}}, rotation =

0)));

inner Modelica.Fluid.System system(energyDynamics =

Modelica.Fluid.Types.Dynamics.FixedInitial) annotation(Placement(visible = true,

transformation(extent = {{60, 60}, {80, 80}}, rotation = 0)));

equation

connect(pipe.port_b, tank2.ports[1]) annotation(Line(points = {{0, -10}, {0, -60},

{40, -60}}, color = {0, 127, 255}));

connect(tank1.ports[1], pipe.port_a) annotation(Line(points = {{0, 34}, {0, 10}},

color = {0, 127, 255}));

end Two_tanks_transfer;

Parameterisation and initialization of the model.

At this stage the user has to insert the parameterisation values for all components. In this case

the physical dimensions of the components, to match the same dimensions of the stated problem

above. An example of this parameterisation step is shown in Figure 3, for the tank1.

Figure 3 – Parameterization of tank1


WaterWatt Public

Simulating the model and plotting

At this stage the program runs the model, and at the end of the simulation process it Plots the

results. In this case it was asked for the model to simulate the dynamic changes of the system for

30 seconds, where it is possible to see the variations of the levels of the two tanks, in the Figure

4.

Figure 4 – Graphic simulation of the first 30 second of the modeled system.

The program is also able to re-simulate the model with other variables and operational conditions.

And as requested it can generate the results and KPI that were defined as outputs of the model.

In the Figure 5 it is shown some of the results that were generated by the bun of the previous

model.

Figure 5 – Results (outputs) of the simulated model.


WaterWatt Public

Production of components that are not in the libraries.

If the component that the user wants to model is not found in the libraries or if he wants to

introduce a significant change, the user will have to produce the code for that model.

In first place the user should produce the physical equations he wants to simulate in the model

and then translate them into Modelica language.

In the following example is shown the physical equations to compute the efficiency of a pump

(Head was calculated by means of Eq. (1) or Eq. (2), in which it was possible to measure upstream

pressure or not (e.g., pumps were located immediately above a storage tank), respectively.

𝐻 =(𝑃𝑒−𝑃𝑖)×100000

𝜌𝑔+ ∆𝑧 , (1)

𝐻 =𝑃𝑒×100000

𝜌𝑔+

(𝑄3600⁄

2

𝜋𝐷2

2 )

2𝑔 +∆𝑧, (2)

where H is the head (m), Pe and Pi are the pressure measurements at downstream and upstream

the pump, respectively (bar), is the water density (kg/m3), g is gravitational acceleration (m/s2),

ΔZ is the difference of elevation between the pump and the tank water level or the difference of

elevation between the two pressure transducers (m), Q is the discharge (m3/h) and D is the pipe

inner diameter (m). Based on collected data, the hydraulic power provided by the pump was

calculated Eq. (3) as well as the global group (pump+motor) efficiency Eq. (4) and the pump

efficiency Eq. (5).

𝑃ℎ =𝑄×𝐻×𝜌𝑔×0.00001×100

3600 , (3)

η𝑔 =𝑃ℎ

𝑃𝑎𝑏 , (4)

η𝑏 =η𝑔

η𝑣×η𝑚 , (5)

where Ph is the hydraulic power (kW), Pab is the absorbed electrical power (kW), ηg is the global

group efficiency (%), ηb is the pump efficiency (%), ηv is the variable speed drive efficiency (%)

and ηm is the motor efficiency (%).

Then the same equation (just equation 1) should be translated into Modelica code, as the

following:

class Pump

import SI = Modelica.SIunits;

import NonSI = Modelica.SIunits.Conversions.NonSIunits;

constant Real G = 9.80665 "Standard acceleration of gravity on earth";

constant Real RO = 999 "water density";

parameter Real Su(unit="m") "Upstream Storage Tank Level";

parameter Real F(unit="Hz") "Motor Frequency";

parameter Real DeltaHm(unit="m") "Difference between Pressure meters";

parameter Real Pu(unit="bar") "Upstream Pressure";

parameter Real Pd(unit="bar") "Downstream Pressure";

parameter Real Q(unit="m3/h") "Flow rate";

parameter Real Pe(unit="kW") "input Power";


WaterWatt Public

parameter Real ESDe(unit="%") "Variable Speed Drive Efficiency";

parameter Real MOTORe(unit="%") "Motor Efficiency";

Real DeltaHPump(unit="bar") "Pressure difference in Pump";

Real H(unit="m") "Net Head";

Real Ph(unit="kW") "Hydraulic Power";

Real EfficiencyG(unit="%") "Global Efficiency";

Real EfficiencyP(unit="%") "Pump Efficiency";

equation

H=(((Pd-Pu)*100000)/(G*RO))+DeltaHm;

DeltaHPump=H*G*RO*0.00001;

Ph=((Q/3600)*DeltaHPump*100000)/1000;

EfficiencyG=(Ph/Pe)*100;

EfficiencyP=EfficiencyG/(ESDe*MOTORe/10000);

end Pump;

10. Examples of WaterWatt models

In this chapter will be presented two examples of the models produced for the WaterWatt case

studies simulation, the first one has produced with the Modelica Standard Library and the second

one with the ThermoPower library.

Example of an open cooling water circuit.

This circuit is an auxiliary circuit of the process of a sugar factory. Sugar must be cooked at low

temperature (70ºC), and to boil water at this temperature it is necessary to produce vacuum. This

vacuum is produced in 7 barometric condensers (one for each (7) cooking recipient). To produce

vacuum, it is necessary to deliver cold water (30ºC) into the barometric condensers, to

condensate the steam into hot water (40ºC).

This hot water is collected in a tank and then is sent, by 4 pumps, to the top of a cooling tower.

Then is cooled again to 30ºC and sent through dedicated pipes, one per each of the 7 barometric

condensers, by 7 pumps.

From this circuit the objective is to know what is the minimum temperature required to condense

water in the barometric condensers, in order to produce enough vacuum with the minimum energy

requirements from the pumps and cooling water fan side.

From this circuit was collected the following data: Rated Power (kW), Instant Power (kW),

Operational Frequency (Hz) of the motors, RPM of the motor and pump (nominal), Nominal

Efficiency, Rated Flow (m3/h), Instant Flow (m3/h), Upflow pressure (bar), Downflow pressur

(bar), Impeler Ø (mm), Net Head, Pipe upflowØ (mm), Pipe downflow Ø (mm), teperatures from

the intake and rejected air of the cooling tower, temperatures of the pipes, length of the pipes.

In the Figure 6, is shown the scheme this circuit.


WaterWatt Public

Figure 6 – Scheme of an open cooling water circuit.

For this circuit the optimization opportunities are mainly related with the water and air flow rates,

therefore enabling controls to adjust/optimize fans and pumps flow to achieve the required

temperature and flow of the cold water. At the present time all the pumps of this factory have

variable speed drives, although they work in just one fixed speed manually adjusted by the

necessities of the production for that day (it was adjusted empirically). What they want to assure

is that they have always vacuum in the barometric condensers. The fan of the cooling tower only

has one speed, although we would like to model the effect of a variable speed drive in this fan. I

don’t know if this is compatible with, assuming a fixed delta-T between the fluid and the dew-point

temperature, but at this stage I think if we simplify, the better.


WaterWatt Public

Example of a water treatment circuit

The main objective of this circuit is to provide fresh water for the factory. Part of the water is

provided by ground water sources. This grand water is stored inside a deposit and by the action

of 2 pumps it is filtered through a sand filter and then sent to the drinking water deposit which also

receive water from the water supplier company. Then the water is sent by the action of three

pumps into a deposit which has a high of 14m above the ground.

From this circuit was collected the following data: Rated Power (kW), Instant Power (kW),

Operational Frequency (Hz) of the motors, RPM of the motor and pump (nominal), Nominal

Efficiency, Rated Flow (m3/h), Instant Flow (m3/h), Upflow pressure (bar), Downflow pressure

(bar), Impeler Ø (mm), Net Head, Pipe upflowØ (mm), Pipe downflow Ø (mm).

Figure 7 – Example of a water treatment circuit.

The optimization opportunities of this circuit is to deliver an operational agenda to choose the best

efficiency scheme to deliver water with the required quality to meet the factory necessities, at the

best energy efficiency possible.


WaterWatt Public

11. Conclusion

The overall the purpose of the models is to optimize the energy consumption of the industrial

water circuits. The scenario that we want to simulate with these models is the normal operational

conditions of the process.

The objective of these simulation models is replicate the outputs of the real circuits with the

imputation of the real data that we collected in situ on each real circuit and have the similar results,

with the same operational conditions of the real circuits.

Then the ultimate step is to perform some modifications, like to change the number of pumps in

the modeled circuit (substitute one pump with two pumps with lower rated power), or substitute a

motor with one with a higher efficiency, and see if the model achieve a better global efficiency

and have a correctly estimate what was the increase/decrease of energy used by this new

configuration of the circuit.

The models delivered with the completion of this task will are essential to create the link between

the technical stage and the evaluation platform stage as they will be later implemented in the E3

Platform. The result of this task will enable the definition of KPI in Task 2.2 and the evaluation of

the potential improvement of the developed measures (Task 2.4).

12. References

Hydraulic Institute, Europump and US Department of Energy's Office of Industrial Technologies.

Pump Life Cycle Costs: A Guide to LCC Analysis for Pumping Systems. 2001

IMechE. Centrifugal Pumps: State of the Art and New Opportunities, first. ed. - Institution of

Mechanical Engineers. Bury St Edmunds and London, UK.: John Wiley & Sons, 2004.

McCormick, G., & Powell, R. Optimal Pump Scheduling in Water Supply Systems with Maximum

Demand Charges. J. Water Resour. Plann. Manage., Vol. 129, No. 5, pp 372–379, 2003.

IMechE - Institution of Mechanical Engineers. Pumping Cost Savings in the Water Supply

Industry. Presented at a Seminar Organised by the Fluid Machinery Committee of the Power

Industries Division, 1989.

UK Department of the Environment, Transport and the Regions. Energy savings in industrial water

pumping systems - Good pracatice guide – 249, 1998.

Bolognesi, A., Bragalli, C., Lenzi, C., & Artina, S. Energy Efficiency Optimization in Water

Distribution Systems. Procedia Engineering, Vol. 70, pp. 181–190, 2014.

Modelica - A Unified Object-Oriented Language for Physical Systems Modeling Language

Specification Version 3.2 Revision 2 July 30, 2013.

Syed Adeel Asghar & Sonia Tariq - Design and Implementation of a User Friendly OpenModelica

Graphical Connection Editor by Institutionen för datavetenskap Department of Computer and

Information Science, 2010.

Project Title: Improvement of energy efficiency in ... · gamification for online self-assessment,...

Documents

Transcript of Project Title: Improvement of energy efficiency in ... · gamification for online self-assessment,...