Mathematical model validation.
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These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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At present time, 2 ethyl‐hexanol represents an important raw material in the production of plasticizers, solvents, oils and additives for diesel fuel, making its industrial scale production process of high importance. The industrial scale production of 2 ethyl‐hexanol is made either through liquid phase 2 ethyl‐hexanal hydrogenation or through gaseous phase 2 ethyl‐hexanal hydrogenation. The liquid phase hydrogenation is preferred on industrial scale due to its advantages [1].
\nThe development of complex, accurate mathematical models is an essential step in the dynamic behaviour analysis without expensive experiments and last but not least in the development and testing of control strategies. To this end, for the 2 ethyl‐hexanal hydrogenation process was developed at first a distributed parameter mathematical model validated using experimental data. It consists of a system of partial differential equations based on mass (total and component) and energy conservation laws. In order to analyse the dynamic behaviour and to emphasize the interactions between the parts of a hydrogenation process a dynamic study was performed. Several scenarios have been carried out in order to evaluate the dynamic behaviour. The one presented in this chapter is the study of the catalyst deactivation effect. The effect of a variation of the input flow temperature of the streams as well as the effect of a change in the reactor load: the volumetric ratio between the 2 ethyl‐hexanal flow rate and the recirculated 2 ethyl‐hexanol flow rate are worth to study. The dynamic behaviour study shows the complexity of the hydrogenation process due to heat, mass and kinetic interactions, which are dependent on the operating conditions, reactor loading as well as on the trajectory from one state to another.
\nUnfortunately, despite their accuracy, detailed, nonlinear mathematical models are too complex for efficient use in controller design so the considered approach is the use of a simple model of the process, which describes its most important properties in combination with an advanced control algorithm which takes into account the model uncertainties, the disturbances and command signal limitations.
\nTo this end, another mathematical model–operational model–is determined based on the main connections between input and output parameters of the process and was obtained based on the result analysis from both simulations step responses related to a distributed parameter model and the experimental data.
\nFor the hydrogenation process presented in this case study, various methods of control where designed: conventional PID control, internal model control (IMC) and robust control in order to find the optimal solution.
\nThe two main control objectives of all the applied control strategies are: (a) to maintain the inlet reactor temperature below an imposed critical value; (b) to ensure a high 2 ethyl‐hexanol (product) concentration. From a technological point of view the reactor inlet temperature can be controlled by modifying the 2 ethyl‐hexanol recirculated flow rate. The product concentration is influenced by the reactant flow rates and also by the catalyst degree of activity which acts as a variable disturbance. The catalyst degree of activity will continuously decrease as the hydrogenation reaction takes place up to the point it needs to be replaced. However, during this period, this effect can be compensated by continuously increasing the input temperature of the reactants.
\nFor analysis purposes, all proposed control structures were implemented in MATLAB/SIMULINK environment. The simulation scenarios are presented in comparison for all designed control strategies. The two main objectives of the scenarios are the set‐point tracking analysis and disturbance rejection analysis. A robustness analysis is also performed.
\nFinally, by analysing all the advantages and disadvantages of the designed control structures the final solution recommended for the control of the 2 ethyl‐hexanal hydrogenation process is the robust control. The concluding remarks are formulated in the last section of the chapter.
\nThe mathematical modelling of an industrial scale process is a complex problem requiring the following steps: (a) Choosing the model structure based on physical knowledge; (b) Determining and estimating the model parameters from the available data; and (c) Accuracy evaluation of the developed model (model validation) based on experimental data.
\nThe model validation step is closely related to the parameter estimation step. As a result of the estimation procedure it must be examined to what extent the model really explains the real plant behaviour. This aspect can be highlighted by exciting the system model with the same input signal and studying the nature and value of the difference between the model output signals and the real plant variables.
\nBased on laws of mass conservation and energy conservation, the mathematical model determined in this section for the hydrogenation process 2 ethyl‐hexanal is nonlinear. A dynamic mathematical model can be used to simulate complex mass transfer phenomena and to understand processes occurring inside the reactor. So far there are various models proposed in the literature [2, 3] for the hydrogenation reaction kinetics, but there is no mathematical model to describe the processes taking place inside the hydrogenation reactor. There is a need to develop equations, to determine the parameters and boundary conditions that form the mathematical model consisting of differential equations with partial derivatives. The spatially distributed nature of the process is generally unnoticed or ignored and the control techniques are applied using conventional approximate models with concentrated parameters, identified by experiments input/output. Because these simple models ignore the spatial nature of the process, they often suffer from the close interaction and apparent delays due to diffusion and convection phenomena inherent in such processes. Hence, the need for a modelling procedure that generates a general model, a model that takes into account the spatial structure of the process variable and is deducted from the input and output measured data.
\nThe production of 2 ethyl‐hexanol through the liquid phase 2 ethyl‐hexenal hydrogenation is depicted in Figure 1.
\nHydrogenation reaction: A—2 ethyl‐hexanal, B—2 ethyl‐hexanal, C—2 ethyl‐hexanol.
As it can be observed, the production of 2‐ethyl hexanol is in fact two successive hydrogenation reactions. Thus, the reaction product of the first hydrogenation reaction—2‐ethyl‐hexanal—is a reactant in the next one. In the hydrogenation process n‐butanol and iso‐butanol can be obtained as side products of some side reactions. Also, symmetric or asymmetric C8 ethers can be obtained through the etherification reaction of butanols. These side reactions are favoured by operating parameters like: input flows through the reactor, inlet temperature. However, for mathematical modelling purposes only the main reaction will be considered and the following assumptions are made: the model parameters are considered to be constant on the radial section of the reactor; both liquid and gas velocity are considered constant; adiabatic reactor; perfect mixing is considered and the chemical reactions occur only at the catalyst surface. Also, in the reaction zone the following phenomena occur: mass transfer through the volume element dz (theoretical plate); 2 ethyl‐hexanal hydrogenation on the catalyst surface and heat transfer through volume element dz.
\nThe component mass transfer is essential in a heterogeneous reactor with several phases (gas, liquid) because the reactants have to pass from one phase to another making the modelling of gas‐liquid‐solid mass transfer process a crucial step. Substances from a gas phase (hydrogen) and a liquid (2 ethyl‐hexanal) are transformed on the surface of a solid catalyst (nickel on silicon). One of the most important factors in the chemical reaction is the reaction rate (reaction kinetics). In the present case there are two rates of reaction: r1, which relates to the hydrogenation of 2 ethyl‐hexanal and r2, which relates to the hydrogenation of 2 ethyl‐hexanal (intermediate product). For this particular case the chosen catalyst is nickel (Ni) based catalyst on a silica (Si) support. Considering the literature studies [2] for the Ni catalyst supported on Si, the best model to express the reaction rate of the proposed model is:
\nwhere enal is 2 ethyl‐hexanal, anal is 2 ethyl‐hexanal, oct is 2 ethyl‐hexanol, H is hydrogen,
In industrial scale heterogeneous reactors, the catalyst ages gradually and gets deactivated to the point it becomes inefficient and needs to be replaced. The dependence between the catalyst activity degree and the reaction rate can be expressed as follows:
\nwhere
The reaction rates expression should also include the temperature dependence of the reaction rate constants and absorption constants, which must comply with the Arrhenius law [4] given by:
\nwhere
The developed total and component mass balance equations are as follows:
\nand the heat balance equations for the liquid and gas phases are
\nAll the parameters are detailed in the nomenclature section.
\nAn important feature of the three‐phase reactors is the hydrodynamic characteristic. One of the hydrodynamics parameters with high influence on the performance of the three‐phase reactor is the pressure loss of the two‐phase mixture. The pressure loss in the functional layer is an important parameter, which depends on the amount of energy required to operate and which also correlates the interphase mass transfer coefficients. The friction pressure loss can be calculated using equation ERGUN [5]:
\nwhere
The dynamic process simulator was implemented in MATLAB programming environment along with graphical extension to SIMULINK. The process simulator, being represented by a mathematical model comprising a system of nonlinear partial differential equations, was implemented as a function‐s: S‐function.
\nTo solve the partial differential equations the finite difference method [6] was used. According to this method, the derivatives where written as finite differences. The solution domain must be covered with a network of nodes in order to apply the proposed method. Theoretically, the approximation of the exact solution will be better if the number of nodes included in the network is greater. To approximate the concentrations, temperature and pressure over time and space (height of the reactor) 100 discretization points (the number of theoretical plates) were chosen, being considered the best choice between the complexity of the model and the accuracy of the results. This method is reasonably simple, robust and is a good general candidate for the numerical solution of differential equations.
\nThe developed nonlinear, distributed parameter mathematical model was calibrated and tested based on experimental data acquired from a functional 2 ethyl‐hexanal hydrogenation reactor at Oltchim S.A. company, Romania.
\nThe calibration process was performed taking into account the constructive characteristics of the hydrogenation reactor. The height of the reactor is approximately 20 m, with a diameter of 1 m, while the height of the catalyst is around 18 m. The reactants (hydrogen – gaseous phase – and 2 ethyl‐hexanal—liquid phase) are fed at the top of the reactor. Perfect mixing can be considered because the reactor is equipped with a fine sifter at the top. The product (2 ethyl‐hexanol) is extracted at the bottom of the reactor. Part of the product is cooled to 90–100 °C in a heat exchanger and recirculated at the top to maintain the inlet temperature below 160 °C. The operating temperature of the reactants is around 100 °C. The plant is also equipped with a heater, to be able to increase the input temperature of the reactants as the catalyst gets deactivated. The flow ratio between the 2 ethyl‐hexanal flow and the recirculated 2 ethyl‐hexanol flow must be maintained at a specific value in order to maintain the output temperature below the critical value. The main reactor operating point is characterized by the following parameter values: hydrogen flow of 1250 – 1300 (m3/h), 2 ethyl‐hexanal flow of 4 (m3/h), recirculated 2 ethyl‐hexanol flow of 24 –32 (m3/h).
\nThe accuracy of the developed mathematical model was tested in different operating points and the comparison between the simulated results and the experimental data in the main operating point is presented in Table 1.
\nParameter | \nSimulated value | \nPlant data | \n
---|---|---|
Hydrogen concentration (Kmol/m3) | \n0.078 | \n0.0549 | \n
2 ethyl‐hexanal concentration (Kmol/m3) | \n0.0021 | \n0.0020 | \n
2 ethyl‐hexanol concentration (Kmol/m3) | \n5.346 | \n5.32 | \n
Output temperature (K) | \n441.6 | \n441.3 | \n
Mathematical model validation.
The standard deviation is between 5 and 10 % in all cases. This indicates the presence of an acceptable systematic error. The evolution of the main parameters can also be observed in Figures 2–4.
\n\nBy choosing 100 discretization points the height of the theoretical plates of the reactor is 0.2m. Taking into account the diameter of the reactor (∼1m) it can be considered that the concentration of 2 ethyl‐hexanol and the temperature of the product are constant along the theoretical plate. However, to prove this assumption, a study was performed on the influence of the number of discretization points on the accuracy of the results. The simulation results are presented in the Figures 5 and 6, considering 80, 100 and 125 discretization points. As can be observed from the figures, there are no significant changes in the accuracy of the model, the differences being only at the second decimal.
\n2 ethyl‐hexanal and 2 ethyl‐hexanal concentration evolution: simulated vs. plant data.
Another important study that needs to be performed to prove that the developed mathematical model captured the hydrogenation mechanism is a dynamic behaviour study. To this end, it is necessary to evaluate that the model captures the effect of catalyst deactivation [7]. By analysing the experimental data it was concluded that the catalyst degree of activity decreases up to 50% after 4 months of continuous functioning. Figure 7 presents the effect of catalyst deactivation on the product concentration by maintaining the reactants input flow and temperature constant.
\n2 ethyl‐hexanal and 2 ethyl‐hexanol concentration evolutions: simulated vs. plant data.
Product temperature evolution: simulated vs. plant data.
2 ethyl‐hexanol concentration evolution: 80, 100 and 125 discretization points.
Outlet temperature evolution: 80, 100 and 125 discretization points.
Catalyst deactivation effect on the product concentration.
It is obvious that the catalyst deactivation influences considerably the quality of the product increasing the production costs. This effect can be diminished if the input temperature of the reactants is gradually increased as the catalyst gets older.
\nBased on the previously presented studies, it can be concluded that the hydrogenation process dynamics ethyl 2‐hexanal is very complex; thus, it is only normal that the resulting model is nonlinear, higher order with distributed parameters. The only problem is that highly complex models are difficult to use in the development of most control strategies, being more appropriate for control strategy testing. For this reason it is necessary to design a simpler, linear operation model to be used in control design. There are two possible approaches. The first one infers model reduction methods and linearization which can be troublesome. In this section is presented a more unconventional approach, developing a simpler operational model based on the main connections between input and output parameters, using experimental identification methods and the developed nonlinear mathematical model. The results will be validated by simulation.
\nTo this end, by analysing the hydrogenation process and based on the process engineers experimental knowledge the main input variables are considered to be: input flow of 2 ethyl‐hexanal, recirculated input flow of 2 ethyl‐hexanol, input temperature of the reactants and hydrogen pressure. The main output variables are considered to be the 2 ethyl‐hexanol concentration and the output temperature which is critical. Nevertheless, the dependence between the input and the output variables is considered to be of second order as follows:
\nThe parameter values are determined using experimental identification methods and considering step variations of the recirculated 2 ethyl‐hexanol flow (
Output temperature evolution: operational model vs. nonlinear model.
Conventional PID controllers are the most common control solution in the industry. Most of the research deals with mono‐variable (single input single output—SISO) processes. However, most industrial processes are by their nature multi‐variable (multi‐input multi‐output—‐MIMO). Using mono‐variable controllers for each output variable, even if it is a solution easy to apply, it will lead to inferior performances. It is possible that despite the fact that each individual PID loop control works, the overall PID control structure to fail. For this reason there is a demand for the development of multi‐variable PID control strategies to compensate the effect of functional interactions between variables from many companies that consider the interactions between variables in multi‐variable systems as the main common problem in the industry.
\nThe hydrogenation process is characterized by the presence of time delays of approximately 30 minutes. The difference between the dead time of each input‐output pair is about 1–2 minutes. For this reason, the dead time is considered to be identical for all input‐output pair. A typical approach to deal with time delay is the non‐delayed output prediction [8, 9]. The non‐delayed output may be estimated and the controller can be computed as for a process without delay. The most popular output predictor is the Smith predictor.
\nCurrently, the 2 ethyl‐hexanal hydrogenation plant is operated using the feed forward control (indirect, open‐loop control). Thus, using methods sometimes simple, sometimes complicated (even in closed loop), the following parameters are controlled and adjusted to the desired level: the 2 ethyl‐hexanal input flow, the recirculated 2 ethyl‐hexanol flow, hydrogen input pressure and the reactant input temperature. At present time, an operator decides whether or not to manually modify the control loops set points to maintain the same process parameters and product specifications. To achieve a more effective operation of the hydrogenation process, both conventional and advanced control methods require a closed‐loop control structure by including the reaction from the output.
\nIn order to develop a multi‐variable PID control, the operational model described in the previous section determined by the Eq. (9) will be used. The desired multi‐variable controller matrix has the following form:
\nwhere
The obtained controllers are described by:
\nFigure 9 presents the closed loop Smith predictor control structure using a PID control.
\nThe next step is to test and evaluate the performances of the developed control strategy by analysing its ability to reject disturbance effects, respectively, the set point tracking capability. In the first scenario, a reference variation of 10 K for the first output value (outlet temperature of the product, Tout) is considered. The simulation results are presented in Figure 10.
\nThe second scenario is designed to test the control system capability to counteract the disturbance effects. Hence, a 6 % step variation in the 2 ethyl‐hexanal input flow is considered for the results presented in Figure 11.
\nClosed loop control structure: conventional PID control in MIMO‐SP structure.
Output temperature reference step variation of 10 K: (a) output temperature evolution and (b) 2 ethyl‐hexanol concentration evolution.
(a) Output temperature evolution and (b) 2 ethyl‐hexanol concentration evolution considering a step variation in the 2 ethyl‐hexanal input flow.
Based on the above results, the effectiveness of the proposed control is emphasized, presenting acceptable overshoot, response time and deviation values, but with opportunities for improvement. Thus, in the third scenario is considered an evolution for a period of 4 months and the catalyst activity degree is decreased up to 50%. Figure 12 shows the temperature and 2 ethyl‐hexanol concentration evolutions in this situation. It can be observed a steady error of 0.06% for the temperature and an error of 1% for the 2 ethyl‐hexanol concentration.
\n\nThe last simulation scenario is conceived in order to test the robustness of the designed control system for process parameter variations: gain variation and time constants variations (Figure 13).
\nCatalyst deactivation: MIMO‐SP PID controls.
Output temperature evolution for reference step variation—nominal case vs. uncertain case (multivariable PID control).
A decrease in the control system performance can be observed, inferring reduced robustness, an aspect that may be improved by using advanced control strategies.
\nThe internal model control has emerged as an alternative to traditional feedback control algorithm feedback output as the simulation methods, mathematical modelling and model validation techniques developed [10]. This method provides a direct link between the process model and the controller structure. The IMC control structure is presented in Figure 14 where
The model of the process is assumed to be equal to the process transfer function matrix presented before in Eq. (9) inferring the need of a Smith predictor structure:
\nIn order to ensure the process decoupling it is necessary to determine the pseudo‐inverse matrix [11] of the steady state gain matrix:
\nThe decoupled process is obtained as:
The last step consists of the IMC controller design using:
where
The obtained IMC controllers are:
\nIMC control structure.
The IMC control system evaluation and testing are presented in comparison to the conventional multi‐variable PID control strategy in order to conclude the results. To this end, the first test scenario consists in the set point tacking analysis. The simulation results are presented in Figure 15.
\nOutput temperature evolution for reference step variation: PID vs. IMC.
The second simulation scenario is focused on the disturbance rejection analysis considering a 0.25 [Kmol/m3] disturbance in the 2 ethyl‐hexanal flow input flow (Figure 16).
\nOutput temperature evolution for a disturbance in the 2 ethyl‐hexanal input flow: PID vs. IMC.
The third simulation scenario evaluates the IMC control system capability to counteract the effect of the catalyst deactivation (Figure 17).
\nCatalyst deactivation: IMC control.
As in the previous case the last test scenario consists in the robustness evaluation of the IMC control system by considering the same variation of the process parameters (Figure 18).
\nOutput temperature evolution for reference step variation—nominal case vs. uncertain case (IMC control).
By analysing the comparative results between the MIMO SP‐PID and SP‐IMC control strategies it can be concluded that the SP‐IMC control strategy outperforms the classical control. Another advantage is that it is easy to design and implement. However, even if the performances are clearly better the robustness to process parameter variations can be improved.
\n\nRobust control can be defined as an attempt to control the uncertain systems (uncertainties). This approach accepts the idea of incomplete knowledge of the process, which has an uncertain dynamic and is influenced by disturbances insufficiently known. If, however, for these uncertainties can be established a mathematical norm, by using the robust control theory a robust, unique, able to meet certain performance specifications (hard or relaxed), controller can designed, respecting the uncertainty domain. Regardless of the method used to determine the mathematical model it is necessary to impose simplifying assumptions so that the obtained model is suitable for controller design. The differences between the real plant and the mathematical model represent modelling uncertainties or errors. Precisely from this view point the choice of robust control algorithms for the hydrogenation process 2‐ethyl hexanal is justified. The robust controller design is a laborious task itself, but as the computational tools evolved the only difficult part left is the process parameter variation range determination. The same process operational model presented in Eq. (9) is used for multi‐variable robust controller design based on
The first step was to determine the process state space representation:
\nThe nominal values of the process parameters are:
\nIt is a well‐known fact that, in real control systems, uncertainties are unavoidable and can negatively affect the stability and the performance of the whole control system. Usually, the uncertainties can be classified in two main categories: disturbance signals (input/output disturbance, sensor/actuator noise) and dynamic perturbations (difference between the actual dynamics of the process and the mathematical model) [12]. The dynamic perturbations are usually caused by inaccurate characteristic description, torn and worn effects and shifting operating points. They are also called ‘parametric uncertainties’ and are represented by certain process parameter variation over a certain value range. In a control system the dynamic uncertainties can be represented in multiple ways. For this particular case the output multiplicative representation is considered showing the relative errors (between the actual system Gp(s) and the nominal model Go(s)) not only the absolute errors: Gp (s) = [I + ∆(s)] Go(s). No matter what type of uncertainty representation is chosen, the actual, perturbed system can be represented like a standard upper linear fractional transform, where the uncertainties are lumped in a single block ∆, a diagonal matrix corresponding to parameter variations (Figure 19).
\nGeneralized structure of the closed loop system.
The interconnection matrix for the considered multiplicative perturbation is:
\nThe uncertainty description is determined in an unconventional manner [13]. By using the experimental identification methods several second order models were determined using experimental data from different points of operation. In this way one can determine the interval for nominal model parameters variations.p11, p12, p33, p34, p55, p56, p77, p78, p87, pb31, pb72, pc12, pc16, pc24 and pc28 represent the computed possible, relative perturbation of the nominal process parameters. Each parameter
The next step is to determine the process mathematical model that takes into account also the model parameter uncertainties, Gmds having the following form [12]:
\nThe block diagram of the closed loop system in Smith predictor structure including the robust multi‐variable controller and the uncertainties bloc is presented in Figure 20.
\nThe MIMO‐SP closed loop control structure using robust controller.
As it can be observed Gmds is nominal model of the process,
It should be noted that choosing the suitable weighting functions is a crucial step in the synthesis of robust controller and usually requires several attempts. By applying the presented method, using MATLAB/SIMULINK environment –
Like for the previous control strategies the first simulation test scenario consists of closed loop simulation evaluation under nominal parameter values. The same simulation scenario was performed for the whole class of systems in the uncertainty domain for a reference step variation for the output temperature. The simulation results show good performances of the developed control structure even considering the process parameter uncertainty domain (Figure 21).
\nOutput temperature evolution—robust controller: nominal vs. uncertain considering a reference step variation.
The second simulation scenario will evaluate the capability to reject the disturbance effect and also to test at the same time the robustness of the system. To this end, a step variation of the 2 ethyl‐hexanal input flow is considered along with the process parameter variations. Good performances are reached even in the case of the uncertain plants (Figure 22).
\nOutput temperature evolution—robust controller: nominal vs. uncertain considering a reference step variation of the 2 ethyl‐hexanal input flow.
Another performance that needs to be evaluated is the ability to counter act the catalyst deactivation effect (Figure 23).
\nCatalyst deactivation: robust controller.
By analysing all the results obtained in the previous figures it can be concluded that even considering the catalyst deactivation steady‐state errors of 0.006% and 0.18% are achieved for the output temperature and 2 ethyl‐hexanol concentration, which are clearly within acceptable limits making the robust control strategy the most suitable for 2 ethyl‐hexanal hydrogenation process control.
\nThe developed model of the hydrogenation process, presented in this chapter, is able to represent the dynamic behaviour of the reactor during operation. Real plant data was used for mathematical model validation. From the dynamic point of view, the system behaves as an element with a large time constant and a large time delay. Hydrogenation multiphase catalytic reactors have complex behaviour and from this point of view, the use of advanced control strategies together with online optimization techniques appears to be a suitable procedure to deal with the problem of operating at high level of performance and safety.
\nA possibility to describe the processes which occur inside the reactor by a linear nominal transfer matrix and uncertainty description is detailed. A practical method for obtaining the uncertainty description is also presented. Three control strategies are proposed, developed, implemented and tested.
\nFinally, a comparison between the advantages and disadvantages of the proposed control solutions is performed.
\nThis work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS– UEFISCDI, project number 155/2012 PN‐II‐PT‐PCCA.
The goal in this chapter is to contribute to theories of consumer behavior in the context of the psychological experience of choice under the conditions of an explosive and expansive sphere of consumption opportunities against the backdrop of the COVID-19 pandemic. During this pandemic, shopping has become much more intensely concentrated in the online virtual environment consisting of digital formats of commercial transactions, and the space of choice for consumers in that online environment has expanded extensively. During the coronavirus crisis, the volume of e-commerce sites offering an assortment of products grew rapidly and their overall activity increased rapidly [1]. Not only did the number of e-shops and online supermarkets increase, but at the same time the sales offering of individual retailers also grew, no longer limited by the physical space of shelves and counters. “Digital tools enable reduced searching costs and provide instant access to a much wider variety of products and services…” [2]. It is this fact of extending the range of shopping options within the digitized formats of eshops that positively contributed during the COVID-19 pandemic to ensuring the availability of requisite supplies and the possibility of their convenient transport directly to homes, during both personal quarantines and area lockdowns. On the other hand, however, in such a situation of abundant choices, what is known as the
COVID-19 significantly reduced the possibilities of conventional offline shopping and limited the volumes of product offerings for some time [2]. However, business transactions moved rapidly to the online environment and supply chains quickly adapted to the indicators of consumer market demand [8]. The temporary problem of lack of product supply due to the reduction of offline shopping was quickly resolved by the rapid conversion to online sales [9]. Thus, COVID-19 did not significantly restrict freedom of consumer choice, but merely triggered its horizontal transformation and shifted its application to the digitalized sphere of shopping. The paradox of choice, originally elaborated by Schwartz [3] and developed in various contexts by a number of other authors [10, 11, 12], applied universally even in the era of the coronavirus crisis, inaccurately equated with the drastic reduction of consumer freedom of choice and the associated frustration of customers.
In this context, I will expose and evaluate the more general and apparently universally operating foci of tension and conflict generated in an environment of an increasingly dense network of consumption opportunities, in which the decisions of actors and the outcomes of choices are confronted with negative subjective experiences of regret, anxiety, or disappointment. Last, I will identify and sequentially explain the main sources reducing satisfaction from consumer choices made in an environment of abundant opportunities. I will focus on the circumstances of the influence of information, aspirations, and hedonistic adaptation as potential sources of their psychological discomfort. These are firmly integrated in the sphere of consumer decisions yet, I presume, are only minimally reflected in the everyday activities of consumers.
This chapter presents a theoretical study based on critical reflection on the discourse regarding changing consumer behavior during the COVID-19 pandemic. The method used to achieve the stated objectives consisted of critical literature review, comparative analysis, and meta-analytical evaluation of selected review and empirical studies aimed at understanding changes in consumer culture and consumer behavior. The critical literature review mainly reflects studies with a sociological, behavioral economic, social psychological, psychological, and partly anthropological focus. At the same time, more detailed attention has been devoted to a critical review of sociological studies from 2020 to 2022 referencing current transformations of consumer behavior during the time of the COVID-19 crisis. Relevant scholarly sources were identified using the ProQuest and ProquestEbooks databases. The methodological framework is built on an attempt to create a theoretical platform of arguments, insights, critical perspectives, and opinions, challenging some stereotypically accepted conceptions of consumer decision-making and freedom of consumer choice in the era of the COVID-19 pandemic. This chapter is intended to prompt future scholarly efforts to empirically investigate patterns of consumption behavior internalized during the COVID-19 crisis and the dynamics of their further strengthening or, conversely, weakening in the post-COVID period. The theoretical conclusions that follow can be developed and further verified through experimental studies and quantitative and qualitative research methods.
Consumption levels fell by around 25% in some European countries (e.g. UK, Spain, Italy, and France) during the coronavirus crisis, while in the USA a 10% drop in consumption was recorded during this period [13]. Over the last 2 years, the COVID-19 pandemic has produced not only dramatic economic but also psychosocial effects, transforming many parameters of consumption behavior and more general lifestyle standards [14]. “Among the consequences of the COVID-19 pandemic, we have seen the closing of shops and other business for months. Consumers have avoided public places, stores, and cultural events, even when such establishments were open. As a result, consumers began to change their purchasing behaviors and habits in a sustainable way” [15].
There is now a relatively rich empirical record from 2020 and 2021 documenting the impact of the COVID-19 pandemic as a source of significant changes in consumer decision-making, shopping patterns, and other characteristics, traits, and manifestations of people’s lifestyles. Silva et al. [16] conducted a detailed review of published scientific studies in journals indexed in the WOS and Scopus databases between 2020 and 2021 with the common research topic of changes in consumer behavior and consumption patterns during the COVID-19 pandemic. The study authors identified a total of 416 relevant articles according to the defined selection criteria (87 from 2021 and 329 from 2020). Based on bibliometric, thematic, and content analysis, the authors identified 7 main topical units referencing lifestyle changes related to consumption behavior during the coronavirus crisis: Changes in consumer behavior; Coping with the lockdowns; Information seeking and sharing; Psychological effects; Addictive behavior; Changes in food consumption; Panic buying and hoarding behavior [16]. Interesting data was also provided by their analysis of the keywords of the studies examined, through which the authors identified three main clusters. In this context of examining the ambivalent nature of proliferation of consumer choices, the following frequently occurring keywords in these clusters are relevant: Consumers; Decision-making; Information-seeking behavior; Stress [16]. In this study, the authors simultaneously addressed the question of other topics and issues that should be explored in greater detail in the context of the effects of the COVID-19 pandemic on changes in consumption behavior. One such key question is the problem of consumer choice and strategies for making purchasing decisions.
An even more extensive theoretical study was conducted by Yin, Yu, and Xu [17] on a robust sample of academic studies published between 1981 and 2021 that report on consumer behavior issues. They analyzed very rich research material, which enabled them to reveal changes in consumption behavior in modern societies over the relatively long time frame of the last decades. The authors point out that the COVID-19 pandemic marked an unexpected, rapid step change in lifestyle and consumption changes. According to their analysis of secondary data, the most significant changes in consumption behavior will occur in the sphere of an increased preference for online shopping or increased interest in healthy foods. They also highlight the importance of the more intensive mix of online and offline commerce, which allows consumers to shop more seamlessly and conveniently from anywhere and at any time. In the context of psychological effects during the coronavirus crisis, other authors confirm the increase in feelings of anxiety and insecurity that stems from online panic shopping and stockpiling, especially of food [18].
A similar meta-analysis was conducted by Smith and Machová [19], who analyzed empirical data from the research agencies Ipsos, KPMG, Roland Berger and Potloc, Salesforce, Worldpay/FIS, and YouGov and reported on actual changes in consumer behavior and attitudes during the COVID-19 pandemic. The authors systematize the analyzed data to identify the main foci of changes in people’s lifestyles and daily practices, including consumption behavior, and describe their key attributes [19]. It is confirmed here that the introduction of restrictive measures in the form of home quarantines and blanket lockdowns has produced dramatic social and economic effects in the populations studied, including a fundamental transformation of consumption practices. Consumer activities have shifted massively to virtual environments, with an increased preference for digital shopping via mobile devices and much greater use of online supermarket delivery apps. It has become clear that shoppers have become much more discerning in their product selection and have reorganized their purchasing decision-making strategies in the course of online shopping. It can be assumed that one of the reasons for this change may be that customers are confronted with a concentration of larger volumes of goods and services in the virtual shopping environment. It is here that potentialities complicating the decision-making process and choice have most likely been amplified for the segment of the population that had been accustomed to the conditions of conventional shopping with a more limited range of offerings in the period before the coronavirus crisis.
Šimić and Pap [13] empirically observed changes in consumption behavior during the coronavirus crisis in Croatia within the Generation Z population, whose members are often referred to as “digital natives”. Based on a quantitative data analysis conducted on a sample of 422 respondents, they showed that the consumption behavior of Generation Z during the coronavirus crisis led to much more stockpiling and overbuying. At the same time, they typically concentrated their consumption activities ever more frequently online, which became a global trend during the COVID-19 pandemic. And yet there was no correlation between changes in consumption behavior and perceived quality of life, which the study authors explain by the fact that for Generation Z, online shopping was already the norm in the pre-COVID-19 era, and as such the reduction in physical shopping options was not perceived negatively as a factor reducing their quality of life. The findings of an empirical study by Wang and Na [20] conducted during the COVID-19 pandemic in three Chinese cities confirm that panic shopping and hoarding, especially of food, is a significant manifestation of similar crises, triggering growing feelings of insecurity and fear of the future. Hesham, Riadh, and Sihem [15] empirically demonstrate statistical associations between age and gender moderating specific changes in consumption behavior in a sample of 360 respondents in Saudi Arabia. According to their findings, interest in healthy foods increased sharply during the coronavirus crisis, especially among women and the elderly population, who were observed to have higher levels of anxiety and psychological distress during the pandemic. Gupta, Nair, and Radhakrishnan [21] offer similar empirical conclusions by looking at changes in consumption behavior in India. There, the COVID-19 pandemic initiated panic and impulse buying and the need to stockpile food. Veselovská, Závadský, and Bartková [22] conducted a sociological investigation on a representative sample of the Slovak population to identify and explain the main factors influencing changes in consumption behavior during the COVID-19 pandemic. By analyzing empirical data, they reach similar conclusions as other authors [23], that in times of crisis, the rate of consumption increases and the allocation of financial resources to savings or longer-term investments decreases. At the same time, the authors of the Slovak study stressed that hygiene/epidemiological restrictions and the related restriction of social interactions have significantly influenced people’s mentality, reorganized daily routines and motivations for action, and, last but not least, modified consumption patterns in terms of a transition to digital shopping formats, which was more evident in the female population than in the male population. A number of other similarly focused empirical and theoretical studies are emerging in the early months of 2022.
In post-industrial societies, the values of material well-being and rising living standards are closely intertwined with the notion of simultaneously maximizing people’s individual freedoms [3]. In other words, existential security and its further strengthening and affirmation in a spiral of increasing abundance should be echoed in parallel at a similarly accelerated and progressive existential level in terms of the emancipation of human freedoms. An integral part of such freedoms is the fulfillment of the premise of a proliferation of choices and decisions in a variety of life situations. It is therefore true that the greater the plurality of choice in each individual decision-making situation, the more intense the personal freedoms people achieve. It should be added that the more freedoms there are, the greater the well-being.
An unbridled offering of products is intended to liberate and emancipate consumers in their ability to make free and authentic choices. In particular, some optimistic scenarios attribute to technological innovation an important function in the creation of abundance in the sense of the ever more voluminous generation of value from fewer resources, but also abundance represented by a more robust selection and variety of options in the areas of everyday consumption, education, and health [24].
There is no doubt that significant expansion of choice as one of the pillars of emancipation of individual freedoms is one of the defining features of the consumer culture of late modern societies. According to Lury [25], it is precisely the trend of accelerated growth in the quantity of types and classes of contemporary goods and the contemporary proliferation of sales and purchasing platforms that forms part of the fundamental parameters of contemporary consumer culture of societal well-being. The expansion of consumption opportunities is fundamentally driven by the increasingly massive conversion of conventional product offerings traditionally determined by the physical context of points of sale, dependent on the personal interactions of sellers and buyers, into the virtual environment of digitized shopping. The online environment of consumer activities is not limited by the space or physical capacity of points of sale and shelves. On the contrary, the virtual shopping environment accelerates the quantitative potential of the assortment of goods on offer and the variability in the selection of types and classes of different products. The digitalization of shopping formats not only contributes to an increase in the quantitative volume of product and service choices, but also to a more creative and personalized shopping experience overall. Thus, consumers are reorganizing their life standards and consumption preferences as a result of the introduction of the technological innovations of digitized shopping [19].
The COVID-19 pandemic has contributed substantially to the speed of these changes, accelerated by the reorganization of consumer shopping patterns and the redefinition of consumption behavior. Everyday life was significantly transformed as a result of widespread lockdowns and home quarantines, as were routine consumer activities. Thus, opportunities for socially interactive individual shopping were reduced, leading to a massive shift of product offerings and sales to online virtual environments [26]. The digitalization of shopping formats has thus directly and indirectly influenced customers’ consumption habits and decision-making strategies [27].
As such, the empirically identified and explicitly described causes of changes in consumption behavior thus undoubtedly include the fact of the forced conversion of conventional shopping to the virtual environment, where the confrontation of customers with the abundance of offerings was intensified and had essentially no other alternative. We can also see changes in the decision-making strategies of customers according to a meta-analysis of empirical data from various reputable public opinion research agencies that tracked various parameters of changes in consumer behavior during the COVID-19 pandemic. “Consumer decision-making and behavior change have rapidly adapted based on a range of individual and contextual characteristics” [19]. At the same time, there should also be evidence of higher levels of customer procrastination and even more demanding product selection criteria from shoppers.
In the spirit of rational choice theory, this is an uncomplicated situation, since every concrete decision and choice made is the result of a stable and reliably functioning hierarchy of the social actor’s priorities and preferences of a social actor, who rationally applies such a system in every similar situation requiring an act of choice, regardless of the number of options needing to be compared and evaluated with each other as part of the implementation of the choice [28].
According to other behavioral economic studies, the conditions of such shopping are not only potentially more creative, varied, and comfortable, but also much more psychologically complicated and even reduce the level of positive feelings about shopping. Masatlioglu and Suleymanova [29] for example, address questions related to the adequate decision-making strategies of consumers and the dangers of procrastination or shopping resignation under the conditions of a dense network of product offerings that should psychologically facilitate choice and practically optimize its outcome. After all, consumers are confronted with numerous psychological and cognitively distorting elements of human thought [30]. While consumers seek to maximize their own utility and assume that their choices in acts of decision-making will lead to this maximization, the outcomes of choice often do not produce the expected effects. In fact, the little-considered reality of the ambivalent nature of consumer culture, sometimes referred to and interpreted as the “culture of overchoice” [31], fundamentally casts doubt on optimistic scenarios referencing theses of increasing consumer comfort and growing feelings of freedom, independence, authenticity, and pleasure resulting from accelerating consumer product choices [24], as assumed, for example, by economic theories of rational choice [32]. While acts of decision-making in an environment of growing choices increase the potential to achieve objectively better, i.e., higher quality, more useful, or more advantageous outcomes, they often instead paradoxically awaken feelings of uncertainty, anxiety, internal tension, disappointment, remorse, or regret [33, 34]. The thesis of a relationship between the escalation of the range of options, the growth of demands for continuous decision-making, and the increasing level of consumer dissatisfaction is also considered at a more general level by other authors [35, 36, 37].
In the conditions of a performance consumer society, active participation in consumption is an indicator of individual success, prestige, and recognition [38]. The function of consumption is simultaneously to construct and reconstruct identities and to model social roles. It is becoming a source of self-reflection and the formation and sharing of symbolic worlds [39]. The consumer culture of affluent societies is equated with a culture of “many opportunities”, providing ever greater volumes of choices and consumption goals in an expanding variety of product offerings. “There are millions of products available on store shelves nowadays” [40]. These conditions then contribute to a conviction that individual freedoms are continually increasing, both in the sphere of the material consumption of shopping itself, and in the dimension of symbolic values and signs, achieved and (re-)defined through different models of consumption behavior.
Consumer culture is characterized by an ambivalent nature. The more diverse and voluminous offering of choices on one hand raises optimistic expectations of expanding individual freedom and independence, while on the other hand it leads to high demands for individual responsibility in making choices and experiencing the outcomes of choices. According to some authors, this very fact leads to negative effects in the form of psychological discomfort, when the degree of inner anxiety and uncertainty and feelings of self-defeat increase as a result of a more complex decision-making process in an environment of many opportunities. Motivations grow stronger to postpone the decision or completely resign from making a choice [41]. On the contrary, similar experiences of negative emotions in the form of remorse and dissatisfaction might not occur in conditions of limited choices. In fact, the outcome of a choice in a situation of limited choices significantly relativizes the feeling of personal responsibility. Each individual decision takes place against a background of minimized consumer choice, and responsibility for the outcome in a context of limited choice can be at least partially shifted to the external circumstances of the system. For example, until the late 1980s, the range of consumer goods in socialist Czechoslovakia was dramatically reduced as the result of its centrally planned state economy to such an extent that something like the psychological discomfort of consumer choice was almost unknown. In such a world, part of the personal responsibility for choices made was thus transferred to an anonymous system of political, economic, cultural, or social parameters of society. Thus, every disadvantageous decision or bad choice need not be experienced as a personal failure. In contrast, a world of hypertrophy of opportunity delegates this responsibility strictly to individuals, who have to deal with the consequences of their own decisions independently. This has not ceased to be the case even during the COVID-19 pandemic, when freedom of consumer choice was preserved in spite of certain expectations and intensively exercised in the online environment of digital shopping formats. For some types of products in particular, freedom of choice was maintained and, in some cases, even enhanced due to the virtual environment.
Feelings of psychological discomfort under conditions of abundant choice are partially caused by opportunity cost. This is a situation where the satisfaction of each individual decision decreases as the number of options increases. For each individual choice at the same time means the rejection of other opportunities that remain unused and untried. Consumers develop fictions and fantasies, imagining hypothetical situations of alternative choices and comparing these with the outcome of a real choice that may appear disadvantageous or unattractive compared to similar imaginings. For example, the average supermarket today offers around 40,000 different items, but the average household needs on average around 150 products to ensure normal operations [42]. This means that the vast majority of the products offered by the average supermarket pass through the filters of consumer choice, at the cost of increasing opportunity cost. In the COVID-19 era, it is possible to consider some reduction in opportunity cost (and a reduced sense of “feeling of missing out”) when consumer choice did not only focus on mainstream consumer products (food, clothing, electronics) but also, for example, on various activities and entertainment requiring social contacts. During the lockdown in particular, the options for paid and unpaid leisure activities were very limited and the space for choice drastically restricted.
In the post-COVID era, we are now witnessing the rapid revitalization of the space of choice in various areas of consumption, which reinforces feelings of individual freedom, yet also implies an increase in transaction costs. According to Mlčoch [43], the decision-making process and each choice made place considerable demands on the time, energy, and cognitive abilities of consumers seeking and comparing information about products, their prices, quality, and countless other characteristics. The increasing transaction costs associated with choice may ultimately lead consumers to resign and definitively refuse to make the planned choice. Vardi [44] illustrates such a situation with the example of Jewish emigrants from the Soviet Union who, after very difficult negotiations with the Soviet authorities, were allowed to emigrate to Israel on a limited basis in the early 1970s. Smaller groups of Soviet emigrants were confronted in Israel with a Western-style economy and a standard of living equivalent to Western welfare standards. According to some memoirs, Jewish emigrants accustomed to the conditions of shopping in the Soviet Union found it difficult to navigate the goods on offer in Israeli supermarkets and often left without making a purchase.
This brings us to the problem where the principle of “more is better” moves actors not toward liberation but rather closer to states of paralysis and passivity. Czech [6] reached conclusions supporting this thesis in the present when studying the functioning of Swedish pension funds in recent decades. While 70 financial companies in Sweden had offered a total of 465 pension funds in 2000, this increased to 800 in 2006; by 2015, 102 companies were involved in the administration of a total of 843 pension funds in Sweden [6]. The consequence of the increasing options for types of pension savings was a delay in potential buyers pursuing such savings and an overall decline in pension savings contracts. For example, Google, following the recommendation of the results of one of its marketing studies, decided to increase the number of links listed on a single page when a specific password was entered. This move was oriented toward accommodating Google’s customers, who had repeatedly expressed in surveys a desire to increase the amount of input when searching for information. When Google tripled the number of links per page, search and information tracking through Google began to plummet [45].
And yet other, namely behavioral economics studies consider this type of paralysis and resignation from making decisions due to being overwhelmed with large volumes of choices to be rather rare [46]. The more significant problem, as they see it, is the implementation of decisions that are not only disadvantageous, but often fatally damaging to the interests of the actors themselves. This is attributed to people’s limited attention spans, their easy manipulability, and the underestimation or unintentional disregard of important product parameters, referencing their price or quality. In general, the behavioral economics perspective accepts the thesis that freedom of choice is not a guarantee of an efficient decision-making process, but only the potential to achieve optimized choice outcomes in terms of pursuing one’s own goals and priorities. The reason is that the effectiveness of the decision-making process is significantly impaired by the limits of people’s cognitive capacities and limited attention. When cognitive resources are depleted and attention is declining, the decision-making process turns into a shallow, intuitive affair, generating many missteps. This is especially true when dealing with information, where increasing volumes of information often do not lead to more efficient solutions and decisions, but rather to suboptimal outcomes and higher overall transaction costs [47].
At a general level, the behavioral and social sciences confirm the thesis that the proliferation of choices fundamentally complicates acts of decision-making, increases costs for consumers, and leads to an increase in indecision and feelings of dissatisfaction. Yet consumers reject potential and actual reductions in choice and experience them as a threat to their freedom of choice, especially for certain types of products [48]. This was confirmed during the COVID-19 pandemic, when the reduction of offline shopping options triggered a strong psychological response from consumers [2, 26]. As a result, business activities were concentrated in the online virtual shopping environment while more or less maintaining the abundance of product choices that consumer markets demanded. At the same time, due to health concerns, consumer demand grew for non-standard distribution channels for the goods purchased [49].
Let us next attempt to summarize and briefly describe the possible effects that may act as complementary and interrelated forces in the extensive field of consumer choices. Why, then, might we feel worse off in situations “when we have more”?
First of all, this is a problem of information. The easy availability and abundance of information is not only a more general defining feature of a contemporary technologically advanced society [50] and a common attribute of everyday behavior, but also an elementary principle of the functioning of consumer culture, where it is reproduced and confirmed by a globally functioning and operating platform of information flows from producers, sellers, and consumers. Decision-making based on easy and quick access to large volumes of information should, according to all the assumptions of rational choice theory, optimize choice or lead consumers to favorable or desirable choice outcomes in terms of their own expectations and desires. And yet behavioral economists point to the practical problem of people’s cognitive limits and their declining ability to gather, organize, compare, and evaluate all available information on different products of interest in a comprehensible way. Thus, more information and escalating choices may not necessarily lead in a linear fashion to greater efficiency in achieving individual goals and making the most advantageous decisions. “However, because of limited attention and cognitive resources, people are not able to use all available information and freedom of choice effectively to achieve their own best interests” [40]. Imagine the amount of information that customers must accumulate, evaluate, and compare in their search for the best possible product choice when, for example, even a single brand of sporting goods in a retailer’s catalog represents more than two dozen different individual parameters in an offering of tens and hundreds of other models of a similar product from other brands [33]. Is it even possible to organize and mutually compare hundreds and perhaps thousands of pieces of information from different quality parameters and features among such a wide range of product offerings?
Consumers are sensitive to this fact; as early as at the stage of decision and the making of the choice itself, they may be anticipating the inner turmoil and uncertainty of the final choice. Recall that this anticipation of internal tension due to a lack of options and means to evaluate all the information available to retailers is based on the knowledge that every choice made also implies a decision not to make alternative choices that may be more advantageous overall or that may prove after some time to have been more advantageous. The fact that consumers decide for the best possible option out of the available choices based of the amount of information available to them is thus accompanied by ongoing uncertainty and doubt, which also reduces the subjective feelings of satisfaction in and enjoyment of the product purchased. “However, the increasing personal anxiety and rising transaction costs associated with informing oneself about choices from an ever-larger set of goods on offer can still be ‘incorporated’ into a standard theory of consumer behavior” [42].
There are, however, at least two other reasons whose functions and meanings are somewhat outside the scope of research attention and are generally neglected even by the “standard” theories of consumer behavior. These are the issues of aspirations and hedonistic adaptation.
We examine the question of aspirations in the form of hopes and expectations of what we want to achieve in the area of consumer welfare. As a rule, these tend to increase in situations of high material security, accompanied by a proliferation of consumption opportunities as an inseparable feature of the rising standard of living in affluent societies. Furthermore, consumer aspirations are systematically and programmatically initiated by a dense network of information flows, images, and messages produced by the advertising industry’s media apparatus and by advanced tools of integrated marketing communication, including the use of sophisticated artificial intelligence technologies. In the media-amplified hedonistic orientation of life, complete with examples and presentations of different variants and models of the attractiveness of lifestyles, the ethos of “a life of unlimited possibilities”, “a world without limits”, “a life of infinite opportunities” is awakened, which inevitably widens the gap between the reality (what we actually achieve) and the possibility (what we would like to achieve).
Lastly, there is the problem of (hedonistic) adaptation, which is closely related to the effects of increasing aspirations. Hedonistic adaptation, in the case of consumption, is what subsequently weakens the intensity of the initial enjoyment and the pleasure from the goods acquired (we find interesting similarities here with Weber-Fechner’s law defining the relationship between psychic stimulus and perceived change—if the intensity of a stimulus grows by a geometric order of magnitude, then the intensity of the sensation grows by an arithmetic order of magnitude).
Behavioral economics here assumes that people are emotionally adaptive, finding support for this claim in Brickman and Campbell’s psychological theory of hedonistic adaptation [51]. Thus, achieving a higher degree of consumer well-being may cause a certain fluctuation or deflection in the level of subjective happiness, however this returns to its original level after a certain period of time. Many consider that the achievement of a feeling of happiness lies in notions of fulfillment of aspirations, and yet once the goalposts are passed and these aspirations realized, they are quickly forgotten and cast into the past as unnecessary artifacts of one’s own biography. This explains why there is such fervent pursuit of ever higher standards of living in affluent societies, why people endeavor to make their material comfort even more “comfortable” and convenience ever more “convenient”. The past is always judged from the perspective of a higher aspirational level, and perhaps we too easily succumb to the illusion of the added value of well-being to a future from which perhaps too much is expected.
Hedonistic adaptation seems to operate at another level as well. Namely, consumers may exhibit a decreased ability to predict the chilling effect of adaptation due to higher expectations, also based on their own belief that their choice will be the “best” choice (depending on their ability to obtain, compare, and evaluate information). This contributes to the optimistic scenario of hoping that the choice will not bring disappointment, but rather longer-term feelings of satisfaction. However, these aspirations mean that the effects of hedonistic adaptation will weigh all the more heavily on this group of consumers. When one considers how quickly the costs associated with seeking the best price for a product are “amortized” over time as a result of hedonistic adaptation, their losses seem all the greater.
Consumer culture does not consist solely of a specific type of material culture and does not only express systems of relationships to material values. It represents a world of symbols and signs that transforms material goods into their immaterial meanings, including the creation of identities, sources of self-reflection, and modifications of social roles, including the definition and redefinition of social relationships. Consumer culture is subject to changes of varying intensity, depth, and duration. The most significant transformations of recent decades would include not only the democratization of consumerism, but also the expansion of consumption opportunities and the unprecedented abundance of consumer choices. Consumer culture is characterized by its ambivalent nature. In the spirit of rational choice theory, the proliferation of choices is a positive and universally useful phenomenon, which also promotes a desired emancipation of individual freedoms. However, from the perspective of behavioral economists and many sociologists and social psychologists, this phenomenon is problematic and highly ambiguous, as it generates social and psychological risks that are unseen and difficult to predict. What was originally a rational and generally accepted requirement for the constant expansion of the space of choice has become an irrational desire with considerable potential to harm all those concerned. In this context, the “more is better” principle is a significant complication for consumers, where it is increasingly difficult to operate without experiencing cognitive dissonance, self-blame, regret, and feelings of self-defeat. Moreover, empirical research during the COVID-19 pandemic has demonstrated the importance to consumers of feelings of safety and security, which will likely be a central theme of the shopping experience in the post-COVID era [22]. At the same time, there are many overlooked arguments to support the claim that the limitation of consumer choice during the COVID-19 crisis occurred only partially and only in the conventional shopping environment. And yet the freedom of consumer choice for certain types of products was maintained and even enhanced in the virtual shopping environment. The psychological discomfort associated with choice in an environment of many opportunities was therefore not eliminated and may have contributed to the overall psychological discomfort and mental distress during the lockdown. However, during the COVID-19 era opportunity cost was decreasing, particularly for paid forms of entertainment and leisure activities involving social contact (concerts, sports matches, etc.).
At present, we have an opportunity to observe many social initiatives, the dematerialization movement, and numerous spontaneous civic manifestations whose appeals have intensified precisely at the time of the COVID-19 pandemic, and in recent weeks in the context of the war in Ukraine, rising inflation, and the scarcity of some strategic raw materials. These call for changes in the politics of lifestyles in the spirit of the principle of “less is more”, a transformation of value orientations appealing to ecological and environmental responsibility, solidarity, and accountability, voluntary frugality, life minimalism or alternative hedonism as a return to the roots of the philosophical agenda of Epicureanism, in which hedonism was defined by “modest materialism and tranquility”. The rule should be to live a rich life by modest means. In these transformations of life attitudes and value worlds, it is not only the actual patterns of consumption behavior and the motivations for consumption decisions that are fundamentally changing for individuals and groups, but also the deeper layers of their identities, which will seek new sources of affirmation in the environment of consumer culture markets. The question then remains as to what form these sources of identities will take and in what direction they will be further developed in terms of the interactions of markets and consumers, such that markets may retain the direction of “more is better” or all the preconditions of economic prosperity and growth as the condicio sine qua non of their existence, while at the same time offering sufficiently credible sources of social identities to newly emerging alternatives to (counter-) consumerism, intertwined in many ways with its radical reduction and rejection. Thus, it is not only consumers in decision-making and choice implementation situations that find themselves in an ambivalent situation, but also the markets themselves, as well as the accompanying systems of marketing support for consumer culture that respond to current and future lifestyle politics.
The result was created with the use of institutional support for long-term conceptual development of research of the University of Finance and Administration.
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Hoffmann, N. Fainer, M. Kosinova, O. Baake and W. Ensinger",authors:[{id:"56722",title:"Dr.",name:"Peter",middleName:null,surname:"Hoffmann",slug:"peter-hoffmann",fullName:"Peter Hoffmann"},{id:"56726",title:"Dr.",name:"Marina",middleName:null,surname:"Kosinova",slug:"marina-kosinova",fullName:"Marina Kosinova"},{id:"56727",title:"Prof.",name:"Wolfgang",middleName:null,surname:"Ensinger",slug:"wolfgang-ensinger",fullName:"Wolfgang Ensinger"}]},{id:"60792",doi:"10.5772/intechopen.76062",title:"TCAD Device Modelling and Simulation of Wide Bandgap Power Semiconductors",slug:"tcad-device-modelling-and-simulation-of-wide-bandgap-power-semiconductors",totalDownloads:2116,totalCrossrefCites:15,totalDimensionsCites:15,abstract:"Technology computer-aided Design (TCAD) is essential for devices technology development, including wide bandgap power semiconductors. However, most TCAD tools were originally developed for silicon and their performance and accuracy for wide bandgap semiconductors is contentious. This chapter will deal with TCAD device modelling of wide bandgap power semiconductors. In particular, modelling and simulating 3C- and 4H-Silicon Carbide (SiC), Gallium Nitride (GaN) and Diamond devices are examined. The challenges associated with modelling the material and device physics are analyzed in detail. It also includes convergence issues and accuracy of predicted performance. 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However, the clamping force is quite important for PP IGBTs because too much clamping fore will cause mechanical damage to the silicon chips and too little clamping force will increase the junction temperature of the silicon chips due to the increased thermal contact resistance. And eventually it leads to thermal damage. Furthermore, the clamping force distribution within PP IGBTs is affected by many factors, and they can be divided into the internal and external factors. The finite element analysis model of the PP IGBTs is established based on the theory of elastic mechanics to obtain the influence of the affect factors, including the external clamping modes, spring design, thermal stress, the machining accuracy, and so on. The contribution of those affect factors to the clamping force distribution is ranked, and this can be a guideline not only for users but also for the manufacturers.",book:{id:"6695",slug:"design-simulation-and-construction-of-field-effect-transistors",title:"Design, Simulation and Construction of Field Effect Transistors",fullTitle:"Design, Simulation and Construction of Field Effect Transistors"},signatures:"Erping Deng, Zhibin Zhao, Jinyuan Li and Yongzhang Huang",authors:[{id:"234852",title:"Dr.",name:"Erping",middleName:null,surname:"Deng",slug:"erping-deng",fullName:"Erping Deng"}]}],mostDownloadedChaptersLast30Days:[{id:"60792",title:"TCAD Device Modelling and Simulation of Wide Bandgap Power Semiconductors",slug:"tcad-device-modelling-and-simulation-of-wide-bandgap-power-semiconductors",totalDownloads:2116,totalCrossrefCites:15,totalDimensionsCites:15,abstract:"Technology computer-aided Design (TCAD) is essential for devices technology development, including wide bandgap power semiconductors. However, most TCAD tools were originally developed for silicon and their performance and accuracy for wide bandgap semiconductors is contentious. This chapter will deal with TCAD device modelling of wide bandgap power semiconductors. In particular, modelling and simulating 3C- and 4H-Silicon Carbide (SiC), Gallium Nitride (GaN) and Diamond devices are examined. The challenges associated with modelling the material and device physics are analyzed in detail. It also includes convergence issues and accuracy of predicted performance. Modelling and simulating defects, traps and the effect of these traps on the characteristics are also discussed.",book:{id:"6625",slug:"disruptive-wide-bandgap-semiconductors-related-technologies-and-their-applications",title:"Disruptive Wide Bandgap Semiconductors, Related Technologies, and Their Applications",fullTitle:"Disruptive Wide Bandgap Semiconductors, Related Technologies, and Their Applications"},signatures:"Neophytos Lophitis, Anastasios Arvanitopoulos, Samuel Perkins and\nMarina Antoniou",authors:[{id:"236488",title:"Dr.",name:"Neophytos",middleName:null,surname:"Lophitis",slug:"neophytos-lophitis",fullName:"Neophytos Lophitis"},{id:"247344",title:"Dr.",name:"Marina",middleName:null,surname:"Antoniou",slug:"marina-antoniou",fullName:"Marina Antoniou"},{id:"247347",title:"Mr.",name:"Anastasios",middleName:null,surname:"Arvanitopoulos",slug:"anastasios-arvanitopoulos",fullName:"Anastasios Arvanitopoulos"},{id:"247349",title:"Mr.",name:"Samuel",middleName:null,surname:"Perkins",slug:"samuel-perkins",fullName:"Samuel Perkins"}]},{id:"61629",title:"GaN-Based Schottky Diode",slug:"gan-based-schottky-diode",totalDownloads:1662,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Schottky diode, also known as Schottky barrier diode (SBD), fabricated on GaN and related III-Nitride materials has been researched intensively and extensively for the past two decades. This chapter reviews the property of GaN material, the advantage of GaN-based SBD, and the Schottky contact to GaN including current transporation theory, Schottky material selection, contact quality and thermal stability. The chapter also discusses about the GaN lateral, quasi-vertical and vertical SBDs, and AlGaN/GaN field effect SBDs: the evolution of the epitaxial structure, processing techniques and device structure. The chapter closes with challenges ahead and gives an outlook on the future development of the GaN SBDs.",book:{id:"6625",slug:"disruptive-wide-bandgap-semiconductors-related-technologies-and-their-applications",title:"Disruptive Wide Bandgap Semiconductors, Related Technologies, and Their Applications",fullTitle:"Disruptive Wide Bandgap Semiconductors, Related Technologies, and Their Applications"},signatures:"Yaqi Wang",authors:[{id:"237104",title:"Dr.",name:"Yaqi",middleName:null,surname:"Wang",slug:"yaqi-wang",fullName:"Yaqi Wang"}]},{id:"53537",title:"FPGA-Based Software-Defined Radio and Its Real-Time Implementation Using NI-USRP",slug:"fpga-based-software-defined-radio-and-its-real-time-implementation-using-ni-usrp",totalDownloads:2796,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In this chapter, we propose a novel design of scalable and real-time data acquisition software architecture for software-defined radio (SDR) using universal software radio peripheral (USRP). The software has been designed and tested in multi-thread model, using LabVIEW, which guarantees real-time performance and efficiency. With the help of this design, we have been able to improve the stability of the system besides providing a reconfigurable and flexible architecture. Wireless transfer of sensitive data using communication is not a very safe option. In this chapter, we aim to provide a safe and private wireless transmission between two terminals using the SDR approach and verifying the results in real-world environment with the use of USRP. The novel design being presented here can be used to transfer (random data, text or an image) encoded with different forward error correction (FEC) codes, which is then verified at the receiving terminal and then decoded accordingly to produce the desired result.",book:{id:"5597",slug:"field-programmable-gate-array",title:"Field",fullTitle:"Field - Programmable Gate Array"},signatures:"Nikhil Marriwala, Om. Prakash. Sahu and Anil Vohra",authors:[{id:"192912",title:"Associate Prof.",name:"Nikhil",middleName:null,surname:"Marriwala",slug:"nikhil-marriwala",fullName:"Nikhil Marriwala"},{id:"198652",title:"Prof.",name:"O.P",middleName:null,surname:"Sahu",slug:"o.p-sahu",fullName:"O.P Sahu"},{id:"198654",title:"Prof.",name:"Anil",middleName:null,surname:"Vohra",slug:"anil-vohra",fullName:"Anil Vohra"}]},{id:"61186",title:"Graphene Field-Effect Transistor for Terahertz Modulation",slug:"graphene-field-effect-transistor-for-terahertz-modulation",totalDownloads:1286,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"The real-world applications of terahertz (THz) technology necessitate versatile adaptive optical components, for example, modulators. In this chapter, we begin with a brief review on different techniques for THz modulation. After that, we introduce the extraordinary features of graphene along with its advantages and disadvantages as channel materials for field effect transistor (FET). We then discuss two types of graphene FET-based THz modulators, one is rigid and another is flexible. The feasibility of the high-quality THz modulators with different graphene FET structures has been successfully demonstrated. It is observed that by tuning the carrier concentration of graphene by electrical gating, the THz modulation can be obtained with relatively large modulation depth, broad width band, and moderate speed. This chapter helps the reader in obtaining guidelines for the proper choice of a specific structure for THz modulator with graphene FET.",book:{id:"6695",slug:"design-simulation-and-construction-of-field-effect-transistors",title:"Design, Simulation and Construction of Field Effect Transistors",fullTitle:"Design, Simulation and Construction of Field Effect Transistors"},signatures:"Qi-Ye Wen, Yu-Lian He, Jing-Bo Liu, Qi Mao, Qing-Hui Yang, Zhi\nChen and Huai-Wu Zhang",authors:[{id:"235512",title:"Prof.",name:"Qiye",middleName:null,surname:"Wen",slug:"qiye-wen",fullName:"Qiye Wen"},{id:"247833",title:"Ms.",name:"Yu-Lian",middleName:null,surname:"He",slug:"yu-lian-he",fullName:"Yu-Lian He"},{id:"247834",title:"Prof.",name:"Zhi",middleName:null,surname:"Chen",slug:"zhi-chen",fullName:"Zhi Chen"},{id:"247837",title:"Prof.",name:"Qing-Hui",middleName:null,surname:"Yang",slug:"qing-hui-yang",fullName:"Qing-Hui Yang"},{id:"247839",title:"Prof.",name:"Huai-Wu",middleName:null,surname:"Zhang",slug:"huai-wu-zhang",fullName:"Huai-Wu Zhang"}]},{id:"53730",title:"High‐Speed Deterministic‐Latency Serial IO",slug:"high-speed-deterministic-latency-serial-io",totalDownloads:1734,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In digital systems, serial IO at speeds in the range from 1 to 20 Gbps is realized by means of dedicated transceivers, named serializer-deserializers (SerDeses). 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