【正文】 are used. In addition, inthis situation the highest energy saving is obtainable bymeans of the fuzzy control with the R407C and is of about13% with respect to the one obtained by means of thethermostatic control. Selecting a value of 10 8C for theoutdoor air temperature, the results in terms of energysaving are practically the same. It is important to note thatthe fuzzy control system allows to reach the temperature ofthe air needed in the cold store and to maintain its oscillationFig. 4. Membership function of the derivative of temperaturedifference in the time.Fig. 5. Membership function of the pressor electric motorsupply current frequency.Fig. 6. Electric energy consumption for R507 using both the fuzzycontrol and the thermostatic control (cooling load ! periodicopening cold store door).C. Aprea et al. / International Journal of Refrigeration 27 (2020) 639–648644in the range ^1 8C。1222。exin。2222。exin。3222。4222。 _mref240。254。va188。va222。_Exdes。 1 2Xidi240。 COP=COPrev。In Fig. 9 a parison in terms of the exergeticefficiency of the whole plant, when the R407C andthe R507 are used, is reported versus the frequency of thecurrent feeding the pressor。7222。6222。exin。5222。cp2 exout。airisproperly evaluated for the evaporator and the condenser.The exergy flow destroyed in the pressor, neglecting theheat transfer with the environment, is evaluated as:_Exdes。 1 2T0Tmt。ev222。ev188。co222。co188。P_ExoutP_Exin188。 sothe fuzzy output is turned into a welldefined analogic signal[32,33]. The control algorithm, based on the fuzzy logic, hasbeen built in a Labview environment. In particular, thisalgorithm provides as an output variable a voltage signalwhich can be continuously used by an inverter to control thepressor speed.Fig. 2. Fuzzy control algorithm block diagram.Table 2Fuzzy algorithm rulesf DTvl l ms h vhlvllmshhd240。 ^ Wattmeter 0–3 kW ^%Electric energy meter 360–420 V。: The fuzzy logic is based on thedetermination of the fuzzyset that represents the possiblevalues of the variables. The fuzzy theory with respect to thetraditional logic theory, according to which an element canbelong or not to a particular set, allows the partialmembership of an element to a set. Each value of thevariables is characterized by a membership value whichchanges with continuity from zero to one. Thus, it is possibleto define a membership function for each variable thatestablishes the membership rate of a variable at a certain set.From an operative point of view, a controller fuzzy receivesthe values of the input variables, performs some operationsand determines an output value. This process is characterized by three principal phases: fuzzification, inferencemechanism and defuzzification. The fuzzification processallows to transform a value defined into a fuzzy value。DT222。 moreover, the testshave been performed both in the winter and in the summerseason. As for the summer tests the outdoor air temperatureat the condenser has been kept at about 32 8C thanks to achannel where the air is heated by means of an electricheater, while in winter the outdoor air temperature has beenkept at 10 8C. The experimental results are mostly presentedin terms of electrical energy consumption, measured bymeans of an opportune electric energy meter, evaluating theenergy saving obtained when a pressor speed control isused. The tests, which lasted 2 days, have been realized forthe R407C and the R507.4. Fuzzy logic in the pressor speed controlThe fuzzy logic represents a methodology that allows usto obtain defined solutions from vague, ambiguous oruncertain information. For this the fuzzy process is verysimilar to that of the human mind capable of finding definedconclusions starting from approximated information anddata. In contrast to the classic logic approach, that requiresan exact definition of the mathematical model equationscharacterizing the phenomenon, the fuzzy logic allows us tosolve problems not well defined and for which it is difficult,or even impossible, to determine an exact mathematicalmodel. Therefore, the human experience and knowledge isnecessary for this type of modelling. In particular, the fuzzylogic is a valid alternative for the solution of nonlinearcontrol problems. In fact the nonlinearity is treated bymeans of rules, membership functions and inferentialprocess, that ensure simpler implementations and minordesign costs. On the other side the linear approximation of anonlinear model is simple enough, but it has thedisadvantage to limit the control performances and canresult, in some situations, expensive. Moreover, the fuzzycontrollers are robust and allow us to realize improvementsor changes in a very simple way by means of the use of theother rules or the membership functions. Many examples offuzzy control can be found in some recent applications. Inparticular, in the heating ventilation and airconditioningindustry there are various fuzzy control applications of theair temperature and humidity [25–28]. The design of afuzzy controller requires three essential phases. The first isto establish the input and output variables. The second is todefine the membership functions for the input and outputvariables. The last is to select or formulate the control rules.The main goal of this paper is to determine a fuzzycontroller capable of regulating the pressor electricmotor supply current frequency. In Fig. 2 a block diagram ofthe fuzzy control process of the mercially avalaible coldstore air temperature is reported. In particular, the figureshows a twoinput oneoutput fuzzy controller. The inputvariables are the temperature difference between the setpoint temperature and the real temperature of the air in thecold store 240。 all this generally results in arob