太阳能增强型工业供暖系统的设计外文文献

Design optimization and control approach for a solar augmented industrial heating Fitsum Bekele Tilahun a Ramchandra Bhandaria Mengesha Mamob aInstitute for Technology and Resources Management in the Tropics and Subtropics ITT TH K oln University of Applied Science Betzdorfer Strasse 2 50679 Cologne Germany bAddis Ababa University Institute of Technology King George VI Street Ethiopia a r t i c l ei n f o Article history Received 19 September 2018 Received in revised form 16 April 2019 Accepted 21 April 2019 Available online 24 April 2019 Keywords Solar industrial heating Design optimization Dynamic control Solar fraction Payback period Carbon mitigation a b s t r a c t Process level integration of solar energy could give an economically feasible solution if the industrial process allows its practical integration The solar augmented industrial process behaves as a complex system infl uenced by uncertainty of solar radiation variability of demand temperature and process time schedule as well as possibility of thermal stratifi cation in the storage Addressing these issues to reach the most economical solution has two dimensions to it First the solar thermal system needs to be optimally designed This requires the development of a performance criterion that will deliver maximum solar energy to the industrial process avoid large variations of energy in the storage and meet invest ment constraints Second the identifi ed optimal system should be dynamically controlled to enable uniform heat distribution and effi cient auxiliary heat utilization This paper presents a holistic design optimization and control approach for a solar augmented industrial process to facilitate decision support The proposed solution is designed and optimized for a dyeing industrial process case study that resulted in a 5 7 year payback period 56 3 solar fraction and 252 2 tons equivalent carbon emission reduction Furthermore by implementing dynamic control about 12 4 increase in solar gain that led to a 5 6 reduction in payback period is identifi ed 2019 Elsevier Ltd All rights reserved 1 Introduction Global estimates of industrial energy use indicate that more than 66 7 of the total energy consumption is for process heat out of which 50 is for low to medium temperature range 1 Ac cording to the same study about two fi fths of primary energy use in these applications comes from natural gas or petroleum indi cating a solar replication potential that is estimated to be 15 EJ This would cover about 10 of industrial energy demand Solar heating technologies for low to medium temperature range applications are considered to be readily available 2 Currently several papers have identifi ed promising industrial sec tors for solar process heat integration For instance the replication potential of solar thermal for extraction and refi ning processes in oil industries are estimated to be 44 GWth and 95 GWth respec tively 3 A similar approach found out about 11 GWth replication potential of solar heat in textile industries 4 In Ref 5 the economic as well as environmental benefi t of integrating solar thermal to agro processing industries has been demonstrated The carbon emissions reduction potential of solar industrial process heating in paper industries is estimated to be about 0 34 million tons per annum 6 Techno economical assessment of solar ther mal use in Electrowinning process showed the need for considering several scenarios to arrive at a meaningful payback period 7 On the other hand solar process heat integration in diary 8 and brewery 9 industrial sectors shouldinclude heat recoveryscheme Processlevelintegrationofsolarenergycouldgivean economically feasible solution in low to medium temperature range industries if the industrial process allows its practical inte gration In this confi guration a variable auxiliary heat is incorpo rated to control the temperature of the process In addition owing to magnitude and temporal variation in process heat demand a storage tank is often coupled to the solar system to enhance its effi cacy As a result the solar augmented industrial heating system behave as a complex system infl uenced by the uncertainty of available solar radiation 10 variability of demand temperature and process time schedule 11 as well as possibility of thermal Corresponding author E mail address ftsebeek F B Tilahun Contents lists available at ScienceDirect Energy journal homepage https doi org 10 1016 j energy 2019 04 142 0360 5442 2019 Elsevier Ltd All rights reserved Energy 179 2019 186e198 stratifi cation in the storage tank 12 Moreover the adoption and replication potential of solar process heating system is also largely affected by its fi nancial feasibility 13 Addressing these issues to reach at the most economical solution has two dimensions to it First the solar thermal system need to be optimally designed This requires the development of a performance criterion that will deliver maximum solar energy to the industrial process avoid large variations of energy in the storage and meet investment con straints Consequently the optimization problem leads itself to a non linear objective function that results in complex interaction of design variables in large solution space 14 This fact has forced researchers to simplify the solar dynamics and use simpler objec tive functions that attempt to minimize costs such as payback period 7 total annualized cost 8 and annualized life cycle saving 15 The aforementioned diffi culty could be due to the inability of existing solar modeling tools to accurately represent the thermo dynamic system 16 This could also relate to the practical infea sibility of carrying out the optimization problem for one year with hourly or less resolution solar radiation and demand data 17 The software limitation however could be addressed by utilizing cur rent advanced software platforms that support multi method modeling 18 19 such as system dynamics SD discrete events DE and agent based models ABM In this way for example a hybrid industrial solar system could be modelled with an SD for continuous thermal dynamics and DE for industrial processes and associated events To address the second problem various authors works in related topics such asclustering of model data into a smaller subset 20 or generating new representative datasets 21 in place of the original data could be implemented The other dimension is the energy effi ciency improvement gained as a result of continuous control of the heat transfer in the identifi ed optimal system The on off fi xed rate control which is prevalent in most solar operated thermal systems is simple to implement 22 but results in highly ineffi cient control of the fl uid temperature 23 On the other hand modulating the fl ow and hence controlling the fl uid temperature results in high system ef fi ciency due to less auxiliary heat demand and uniform heat dis tribution 24 Even tough dynamic control is implemented for solar domestic hot water 25 desalination plants 26 electricity generation 27 and high temperature industrial applications 28 its use is generally overlooked for low temperature solar augmented in dustrial heating In this paper a holistic design optimization and control frame work for low temperature solar augmented industrial heating is considered and elaborated to facilitate decision support First a thermodynamic model that captures all factors pertinent to low temperature process integration of solar energy is considered Following the modeling an optimizationprocedure that maximizes solar energy supply to an industrial process under payback time constraint is carried out This procedure resulted in optimal confi guration parameter values for collector area storage volume and fi xed fl ow rates to and from collector and storage The identi fi ed optimal values are then imported back to the simulation environment to investigate the solar fraction payback time and Co2 emission reduction potential of the identifi ed optimal system Finally a dynamic control scheme is considered to fi nd out the identifi ed optimal system s thermal performance under a variable fl ow rate control To demonstrate the validity and practicality of the Abbreviations SDsystem dynamics DEdiscrete events ABMagent based models FPC fl at plate collectors ETCevacuated tube collector CPCcompound parabolic concentrator FOfurnace oil RErenewable energy CMcontrol method Nomenclature Qenergy J Qheat W ISolar radation W m2 Cheat capacity J Kg C U overall heat loss coeffi cient W m2K K heat transfer coeffi cient W m2K TTemperature C k incidence angle modifi er mmass kg m fl ow rate kg S Aarea m2 Vvolume m3 Nnumber of storage segments hsegment length m ttime S Slaplace transform variable H Gtransfer function Ycost V iinterest rate Lproject life time year Ppayback period year Greek letters Heat exchanger effectiveness rdensity of water Kg m3 Uperformance index dweight factor h effi ciency ttime constant Subscripts ccollector auxauxiliary aambient feedfeed water lossloss dmdemand bdirrect ddiffussed iinlet ooutlet sstorage eheat exchanger sgsegment OPoperation mainmaintenance FOfurnace oil Iinvestment Ttotal F B Tilahun et al Energy 179 2019 186e198187 proposed solution a case study is conducted on a dyeing industrial process for an Ethiopian textile industry The rest of this paper is organized as follows Four main modules of modeling optimization dynamic control and feasibility analysis of the proposed model are presented in Section 2 Simulation results and model validations are shown in Section 3 Section 4 highlights theresearch fi ndingsanddiscussesresearchissuesfor improvements 2 Methodological framework The structure of the implemented method consisting of fi ve basic procedures is depicted in Fig 1 and is explained next 2 1 Data and assumptions This stage deals with gathering relevant data as well as trans forming the raw data into appropriate format for further processing in the modeling and optimization steps The following section de scribes the details of the process 2 1 1 Computational tool Two modeling and computational platforms are used in this work Anylogic 29 for modeling and optimization and Matlab 30 for solar data processing and controller implementation Anylogic computational platform is chosen because it is an object oriented modeling framework suitable for complex systems that support SE and DE Moreover Anylogic has a metaheuristics optimization engine based on the OptQuest package from OptTek Systems that allow parameter variation experiments calibration of parameters optimization and Monte Carlo runs 2 1 2 Solar data clustering Due to the complexity of the optimization problem and pres ence of many optimization variables and constraints carrying out the optimization problem encompassing all data points is practi cally infeasible However it is possible to select typical days that are representative of the data sets Forcurrenttask Matlab s embedded k medoids algorithm based on 31 is used for clustering These clusters which are a subsetof the original data are generatedbased on a criterion that indicates their relative similarity One such cri terion that is used in the present work is the mean squared error of the data points 2 1 3 Low temperature solar collector technology Low temperature solar collector technologies for integration of solarheat for industrial processes are fl at plate collectors FPC 32 evacuated tube collector ETC 33 and compound parabolic col lector CPC 34 All of these collectors can be modelled similarly and customized through their parameters to represent a particular collector type The choice of a particular collector for industrial application depends on the temperature level of the industrial process and cost of the technology The FPC is suited for processes having temperature level below 65 C and is also the cheapest among the currently available solar collector technologies ETC and CPC can be used for higher temperature however the former is the preferred choice when cost of the technology is a signifi cant deci sion factor The solar collector technology used for performance modeling and economic evaluation in the present work is Apricus APSE 30 ETC from Apricus Solar Co Ltd APSE 30 is listed in the Solar key mark database for certifi ed products 35 Table 1 shows the per formance parameters of the chosen ETC 2 2 Solar augmented industrial process modeling This section presents the generation storage and demand modeling in the solar augmented industrial heating followed by the optimization and economic feasibility analysis 2 2 1 Solar collector performance model The schematic of the implemented solar augmented industrial heating is shown in Fig 2 Energy balance can be used to describe the solar thermal system dynamics shown in Fig 2 as dQ dt Qc Qaux Qfeed Qloss Qdm 1 where the energy dynamics depend on the solar heat gain from the evacuated collector Qc the auxiliary heat supply Qaux feed water enthalpy Qfeed the system losses Qloss and the heat demand of the dyeing process Qdm The useful heat collected from ETC can be calculated from 1 2354 Solar augmented industrial heating study approach Performance evaluation Dynamic Control Optimization ModelligData collection and processing Generation Storage Consumption Heat Transfer control Solar fraction Payback CO2 Optimal configuration Fig 1 Diagrammatic illustration of the different steps involved in solar augmented industrial heating design Table 1 Performance parameters of APSE 30 ETC 35 Performance related to aperturePar h0a1a2kqd Value0 7101 7370 0081 382 Incident angle modifi erAngle20 30 40 60 kqb1 000 990 980 94 Angle20 30 40 60 kqb1 021 081 331 51 Collector area Aperture 2 84m2 Effective thermal capacity94 3KJ m2K Flow rate per aperture area 1 2 g Sm2 F B Tilahun et al Energy 179 2019 186e198188 Qc Ac h h0kqb q Ib h0kqd q Id a1 Tc t Tc a t a2 Tc t Tc a t 2 i 2 whereAc Ib Id Tc t Tc a t represent the collector area direct and diffused solar radiance on the collector surface collector and ambient temperature respectively The collector temperature is taken as the average value between collector inlet and outlet temperature The optical effi ciencyh0 and thermal loss coeffi cients a1 a2 are specifi cations that depend on the particular ETC used Equivalently Eq 2 can also be described using the collector fl ow rate mc the specifi c heat capacity Cpand changes between inlet Tc i t and outlet Tc o t temperature of the internal fl uid in the collector as Qc mcCp Tc o t Tc i t 3 The inlet and outlet temperatures in Eq 3 depend on the storage outlet temperature Ts o that in turn depends on the heat exchanger effectiveness as Tc o Tc i Tc o Ts o 4 The thermal dynamics in collector is given by Ref 8 dT t c dt 1 rcVc h Ac h0I t Uc T t c a T t c mc T t e c T t c i 5 where Vc rc Uc Te c t are the collector volume m3 density of collector fl uid kg m3 overall heat loss coeffi cient of the collector W m2K andcollectorsideheatexchangertemperature respectively Eq 6 describe the thermal dynamics between collector tem perature Tc t and collector side heat exchanger temperature Te c t 23 dT t e c dt 2 C eme rcCpVe 2 4Cp mc T t c T t e c Ke iAe T t e s T t e c AeKe T t e a T t e c 2 3 5 6 where Ce mc Ve Ae Ke i Ke are the specifi c heat capacity of the heat exchanger material J kg K mass of the empty heat exchanger kg heat exchanger volume m3 heat exchanger surface area m2 heat transfer coeffi cient inside the heat exchanger W m2K and heat loss coeffi cient of the heat exchanger W m2K respectively Similarly storage side heat exchanger temperature Te s t and storage to collector output temperature Ts o t together with the ambient temperatures on heat exchanger T t e a can be deter mined from Ref 23 dT t e s dt 2 Ceme rcCVe 2 4Cp ms T t s o T t e s Ke iAe T t e c T t e s AeKe T t e a T t e s 2 3 5 7 where ms is the storage to collector mass fl ow rate kg s 2 2 2 Stratifi ed storage tank model Heat storage is an integral part of solar system to help mitigate the fl uctuating heat produced by solar collector From Eq 3 it can be deduced that the higher the difference between inlet and outlet collector temperature the higher the useful heat gain Thus a stratifi ed heat storage is considered as an effi cient heat store as it can provide lowest temperature connection point to the collector The following valid assumptions are considered to model the stratifi ed storage A cylindrical tank is assumed and is considered as an aggregate of equal volume totally mixed N segments differing only in temperature dynamics Height to diameter ratio of the cylindrical storage tank is also assumed to be 2 The thermal dynamics for the N equally segmented storage tank Fig 2 Schematic diagram of the proposed solar system with auxiliary heat for the dyeing process F B Tilahun et al Energy 179 2019 186e198189 can be determined from Ref 8 msg ncpdTsg n dt Qconv n Qcond n Qloss n 8 where msg n is the segmented volume mass and cp is the specifi c heat capacity of the segment The convective conductive and loss stated in Eq 8 can be determined from the temperature dynamics of the segment Tnand neighboring segments Tn 1and Tn 1 given as Qconv n mn 1cp Tn 1 Tn mncp Tn Tn 1 9 Qcond n AsgK h Tn 1 Tn 1 2Tn 10 Qloss sgn UAs s Tn Ta 11 where mn 1and mn are the up and down fl ow rates coming from feed water and solar system in segment n 2 2 3 Demand model Direct steam injection due to its effi ciency to transport thermal energy is the most commonly employed method to heat industrial process vessels at low temperature These kind of processes are usually cyclical batch processes that may have sub processes within a single batch It is easy to represent such industrial pro cesses in Anylogic using states States representa location of control that is able to respond to external triggers or events For typical application there might be several simple and or composite states with transitions exiting any of these states For example Fi