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In contrast to exergy analysis approach, a simpler and yet accurate approach of equivalent heat engines is proposed where only minimal input information of key processes or cycles of conversion plant are needed, namely the work (Wa) or heat input (QH), the process average of high (TH), and low (TL) temperatures of heat reservoirs. Presenting the example of a CCGT with a nominal fuel energy input of 2000 MW, the respective ideal or Carnot work of temperature-cascaded heat or reverse engines of CCGT are readily computed, for example, the work engines of gas and steam turbines, as well as the bled steam-powered desalination plants (zero physical work output) as shown in Fig. 3.With this approach, the Carnot work of respective heat engines of CCGT can be “decomposed” individually with respect to the maximum temperature difference between two physical limits predicated by the input fuel and the ambient states. Emulating the same Carnot work as per design of actual cycle, it is then normalized to the respective standard primary energy (QSPE) at the common temperature platform. The thermodynamic consistency of the framework could be confirmed by summing all QSPE of cascaded cycles to yield the primary fuel energy at input. It is envisaged that one of the most plausible and optimal co-generation designs of a hybrid power plant with proven seawater desalination processes is illustrated pictorially in Fig. 7. Here, both electricity and low-grade heat sources are produced in-situ, providing the optimal grid power and capacity of potable water. Such an integrated power and water system is designed with maximum temperature cascade (hence minimum dissipative losses) for power generation and low-grade heat utilization.Fig. 7: A pictorial representation of combined cycle gas turbines (CCGT) plant.A pictorial representation of combined cycle gas turbines (CCGT) plant.Full size imageTo recap, the decoupling framework requires two requisites. Firstly, the matching of Carnot work of each cascaded engine of CCGT, as per designed temperatures, to the ideal engines at the common temperature platform for the computation of standard primary energy (QSPE), as shown in Fig. 3. Secondly, by summing all the standard primary energy (QSPE) available from the decomposed engines, one obtains the equivalent calorific value of fuel supplied to the CCGT.Owing to the common temperature platform of decomposed engines, the ratio of Carnot work (Wc) to the standard primary energy (QSPE) is equally applicable to either a single individual engine or all decomposed engines of the CCGT plant, i.e.,$$frac{{mathop {sum}nolimits_{i = 1}^n {left( {W_{{mathrm{C}},,i}} right)} }}{{mathop {sum}nolimits_{i = 1}^n {left( {Q_{{mathrm{SPE}},,i}} right)} }} = frac{{left( {T_{{mathrm{adia}}} – T_{mathrm{o}}} right)}}{{T_{{mathrm{adia}}}}} = left( {frac{{W_{mathrm{c}}}}{{Q_{{mathrm{SPE}}}}}} right)_i$$
(1)
where “i” refers to a specific engines and “n” denotes the total number of engines. The temperatures, (T_{{mathrm{adia}}}) and (T_{mathrm{o}}), are process-average adiabatic flame and ambient temperatures, respectively. As the first and third terms of Eq. 1 are equivalent to the common temperature ratio, i.e., (frac{{left( {T_{{{{{{mathrm{adia}}}}}}} – T_{mathrm{o}}} right)}}{{T_{{{{mathrm{adia}}}}}}}), the terms can be equated to each other and re-arranged to give the fractional form of process heat or work to their respective total, i.e.,$$frac{{Q_{{mathrm{H}},,i}}}{{mathop {sum}nolimits_{i = 1}^n {left( {Q_{{mathrm{H}},,i}} right)} }} = frac{{W_{{mathrm{c}},,i}}}{{mathop {sum}nolimits_{i = 1}^n {left( {W_{{mathrm{C}},,i}} right)} }}$$
(2)
Before moving to illustrative examples, it is noted that those seeking thermodynamic details should consult Supplementary Table 1 supplied in the article where it will be seen that the framework adheres to the Second Law.Electricity-driven desalination processesAs electricity is one of the convenient forms of derived energy, it is used to power work-driven membrane-based reverse osmosis (RO) desalination processes. By defining the 2nd Law Efficiency as (eta ^{primeprime} = frac{{W_{mathrm{a}}}}{{W_{mathrm{C}}}}) for an engine, where the actual work input is normally known via electricity consumption of processes. From the decomposed gas and steam turbines that produced electricity of a CCGT plant, a conversion factor (CF) can now be defined, based on the consumption of the standard primary energy of these engines to the actual electricity output, i.e.,$${mathrm{CF}}_{{mathrm{elec}}} = frac{{mathop {sum}nolimits_{i = 1}^{n = 2} {Q_{{mathrm{SPE}},i}} }}{{mathop {sum}nolimits_{i = 1}^{n = 2} {W_{a,i}} }}$$
(3)
where the subscripts (i = 1) and (i = 2) refer to the contributions from gas and steam turbines of CCGT, respectively. Note that the denominator term is the actual work, Wa. The latter can be related to the Carnot work (WC) via the empirical 2nd Law Efficiency (left( {eta ^{primeprime}} right)) of the respective work producing cycle. Equation 3 can be further expressed as a function based on the common temperature platform ratio and the sum of work-weighted second law efficiency of the processes, i.e., (mathop {sum}nolimits_{i = 1}^{n = 2} {left( {frac{{W_{{mathrm{C}},i}}}{{W_{{mathrm{C}},T}}}eta _i^{primeprime} } right)}).$${mathrm{CF}}_{{mathrm{elec}}} = frac{{mathop {sum }nolimits_{i = 1}^{n = 2} Q_{{mathrm{SPE}},i}}}{{mathop {sum }nolimits_{i = 1}^{n = 2} W_{a,i}}} = left( {frac{{mathop {sum}nolimits_{i = 1}^{n = 2} {left( {frac{{W_{{mathrm{C}},i}}}{{1 – frac{{T_o}}{{T_{{mathrm{adia}}}}}}}} right)} }}{{mathop {sum }nolimits_{i = 1}^{n = 2} left( {W_{{mathrm{C}},i}eta _i^{primeprime} } right)}}} right) = frac{1}{{left( {1 – frac{{T_o}}{{T_{{mathrm{adia}}}}}} right)mathop {sum }nolimits_{i = 1}^{n = 2} left( {frac{{W_{{mathrm{C}},i}}}{{W_{{mathrm{C}},{mathrm{T}}}}}eta _i^{primeprime} } right)}},$$
(4)
Note that the subscripts “c” and “a” refer to the Carnot and actual work, respectively. (W_{{mathrm{C}},{mathrm{T}}}) refers to the total Carnot work of heat engines. The temperatures (T_{{mathrm{adia}}}) and (T_o) are the process-average adiabatic flame temperature (with due allowance for the excess-air combustion) and ambient temperature, respectively.Although Eq. 4 has generated an expression for the desired figure of merit, (frac{{mathop {sum }nolimits_{i = 1}^2 Q_{{mathrm{SPE}},i}}}{{mathop {sum }nolimits_{i = 1}^2 W_{{mathrm{a}},i}}}), this function is a combination of the common temperature ratio platform and the work-weighted second law efficiency, ({mathrm{i.e.}},,bar eta ^{primeprime} = mathop {sum }nolimits_{i = 1}^{n = 2} left( {frac{{W_{{mathrm{C}},i}}}{{W_{{mathrm{C}},{mathrm{T}}}}}eta _i^{primeprime} } right)).Superficially, the inverse of ({mathrm{CF}}_{{mathrm{elec}}}) may appear similar to the conventional energy efficiency of a power plant. However, a closer examination of its derivation reveals a fundamental difference where it employs the standardized QSPE, and not QH. The latter term expresses only the quantitative aspect and makes no allowance for the quality of energy consumed.Thermally driven desalination processesFor a thermally driven multi-effect desalination system (MED), the low-grade heat supplied yielded zero physical work output as of heat engines. Instead, it produces a finite rate of potable water via evaporation and condensation processes. The Carnot work potential of the low-grade steam entering the MED is computed and it is then decomposed to the equivalent standard primary energy (QSPE) at the common energy platform. Hence, the conversion factor (CFth) of MED desalination is defined as the ratio of standard primary energy consumption to the actual heat supply, Qa, i.e.,$$left( {{mathrm{CF}}_{{mathrm{th}}}} right) = left{ {frac{{left( {Q_{{mathrm{SPE}}}} right)}}{{Q_{mathrm{a}}}}} right} = frac{{left( {1 – frac{{T_o}}{{T_{mathrm{H}}}}} right)}}{{left( {1 – frac{{T_o}}{{T_{{mathrm{adia}}}}}} right)}}$$
(5)
QSPE is based on Carnot work which is defined at application temperatures. Whereas Qa is the actual energy supplied at bled steam temperature. Since the steam inlet temperatures to different thermally driven desalination processes are different, hence the CFthermal are determined separately for assorted plants.Using the physically meaningful conversion factors, namely CFelec and CFth, these factors transform the absolute values (quantity and quality) of derived energy consumed by diverse desalination methods to the common platform primary energy consumption, enabling a cross comparison of energy efficiency from all desalination methods. In brief, the thermodynamic framework provides the common energy platform that served two key roles: Firstly, the fractional apportionment of standardized primary energy consumption, conducted on the cascaded processes of CCGT to the respective electricity, low-grade thermal sources, etc., yielded the causal calibrated conversion factors for the derived energy to power all diverse processes in industry. This calibration of conversion factors is performed with the best power plant systems available hitherto. Secondly, the calibrated conversion factors enable the conversion of specific energy consumption of practical desalination plants, consuming either electricity or thermal sources, into a common energy platform of QSPE. The relative consumption of standardized QSPE for water produced from all types desalination methods can now be compared accurately.In conclusion, the common energy temperature platform has been used to evaluate and compare the consumption of standard primary energy (QSPE) by assorted seawater desalination methods. In co-generating electricity and thermal heat sources from the best conversion plant available hitherto, the apportionment of respective QSPE to the derived energy at a common platform embeds their absolute quantity and quality of input fossil fuels. Based on the thermodynamic framework presented here, the causal conversion factors (CFelec and CFth) are devised, enabling the direct conversion of kWhelec or kWhth into the common energy platform of QSPE:- An essential requisite needed for a just comparison of energy efficiency of multifarious desalination processes or methods.Since 1983 till now, the energy efficiency of SWRO methods were shown to be better than thermally driven methods of MSF and MED. Comprehensively, all existing desalination methods were relatively energy inefficient, at specific energy efficiencies spanning between 7 and 16% of the thermodynamic limit of 1.06 m3/kWhSPE. Recent hybrid designs of thermally driven processes have improved significantly with the twofold increase in energy efficiency, from More

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Effects of imposed disturbance and SRT on QQ-based antifouling efficacyFigure 1 shows the fouling rate profiles of MBRs over time under different operating conditions, summarizing the average fouling rates of each phase. Two representative transmembrane pressure (TMP) profiles, the average fouling time, and the number of MBR runs at each phase are also provided in Supplementary Fig. 1 and Supplementary Table 1. In Phase 1, the average fouling rates for Reactors 1 and 2 were nearly the same, i.e., there was no statistically significant difference between the reactors. This confirms that the MBRs were in identical states, in terms of fouling behavior when operated in the conventional mode. In Phase 2, the average fouling rates of Reactors 1 and 2 were also almost the same, showing that the QQ effect on fouling mitigation appears to be insignificant. Unlike previous reports on the QQ effect on antifouling efficacy35, it was unclear whether QQ played a role in fouling control under Phase 2 conditions. One possible reason for the difference is SRT, which will be further investigated and discussed later. After disturbance (2 d starvation with a high shear rate of 103 s−1) was applied to both MBRs at the beginning of Phase 3, both MBRs experienced severe fouling phenomena, with sharp increases in the average fouling rate at >40 kPa/d. No QQ effect was observed in this phase either. The applied disturbance may have caused a drastic change in mixed liquor characteristics, probably including the microbial community structure, while aggravating fouling propensities, which will be discussed further in later sections.Fig. 1: Membrane-fouling rate.a Its variations with time for Reactor 1 (R1) and Reactor 2 (R2). Each data point indicates the average fouling rate for each MBR run. b Average fouling rates with t-test probability (p) values for each phase. Error bars indicate 1 SD. Details on the experimental conditions are provided in Table 2.Full size imageTo examine how SRT affects membrane fouling in the MBRs, we increased SRT from 50 to 75 d in Phase 4. The average fouling rate of Reactor 1 slightly decreased with vacant beads (which contain no QQ bacteria), whereas that of Reactor 2 decreased more significantly with QQ beads (corresponding to 47% of that of Reactor 1). With the longer SRT, it appears that QQ affected biofouling mitigation. When Reactor 1 was switched to conventional mode in Phase 5, its membrane fouling rate was slightly reduced compared to Phase 4. This implies that the vacant beads had no effect on fouling mitigation and in fact may have caused membrane fouling. A previous study also reported that membrane fouling increased when the media added were trapped inside the membrane fibers36. However, Reactor 2 exhibited a notably slower fouling rate, which corresponds to 55% of that of Reactor 1. Thus, the biofouling control due to QQ was evident at long SRT (75 d). The effect of SRT on QQ will be discussed further in later sections, along with the time-series data of MBR operational performance and microbial community.Effects of QQ, disturbance, and SRT on biopolymer productionFigure 2a–d show EPS and SMP variations during MBR operations with and without QQ at different SRT values. The EPS and SMP data normalized to mixed liquor suspended solids (MLSS) are also provided in Supplementary Fig. 2. During Phase 1, when the MBRs were operated in the conventional mode, the EPS-carbohydrate (EPS-C) and EPS-protein (EPS-P) levels were similar in both MBRs (~20 and 80 mg/L, respectively). Notably, however, there was only ~3% probability that the EPS-P level between Reactors 1 and 2 occurs by chance. One possible explanation is that the microbial communities in both the reactors should change as a result of the provision of synthetic wastewater, leading to alterations in metabolic products. A previous study also reported that fluctuations in EPS-P level at the beginning of MBR operation were observed due to bacterial acclimation to new environments13. In Phase 2, the EPS-C concentration decreased by ~13.5% in Reactor 1 compared with that of Phase 1, but decreased by 33.1% in Reactor 2. However, the EPS-P concentrations in both reactors remained virtually unchanged. It seemed that the lower EPS levels with QQ did not virtually contribute to fouling mitigation, possibly because its levels were still too high to make a perceptible reduction in membrane fouling. In Phase 3, the EPS-C concentration in Reactor 1 increased by ~6%, whereas it increased by ~39.3% in Reactor 2. This substantial EPS-C increase may have resulted in severe membrane fouling, even in the presence of QQ beads. A previous study reported that increased EPS production was strongly correlated with environmental stresses such as shear and starvation37. It was thought that the disturbance at the beginning of Phase 3 may have caused a similar phenomenon. When the SRT was increased to 75 d in Phase 4, the EPS levels in the two MBRs decreased. In Reactor 1, EPS-C and EPS-P concentrations declined by ~15.6% and 11%, respectively, whereas their concentrations decreased by ~74.4% and 21.65%, respectively, in Reactor 2. Similarly, previous studies also reported that EPS production was reduced with long SRT values11,38. In Phase 5, EPS-C and EPS-P contents continued to decrease in Reactor 2; however, no further decrease was observed in Reactor 1. The higher EPS content caused preferential attachment of biomass onto the membrane surface so as to form cake layers39. The reduced EPS production associated with the QQ strategy correlates with previous findings35,40. It is thus believed that membrane fouling could be mitigated by the presence of QQ media.Fig. 2: Biopolymer concentrations and mixed liquor characteristics according to phases in the two MBRs.a EPS-carbohydrates (EPS-C); b EPS-proteins (EPS-P); c SMP-carbohydrates (SMP-C); d SMP-proteins (SMP-P); e mixed liquor suspended solids (MLSS); and f floc size. The box is determined by the 25th and 75th percentiles, whereas the whiskers are determined by the 5th and 95th percentiles. The white square symbol inside each bar represents the average value of each parameter.Full size imageThe SMP-carbohydrate (SMP-C) and SMP-protein (SMP-P) levels were also similar in both reactors in Phase 1 (~5 and 4.5 mg/L, respectively). In Phase 2, there were slight changes in both. When disturbance was applied in Phase 3, the SMP levels in both reactors significantly increased. The SMP-C and SMP-P concentrations in Reactor 1 increased by ~45.3% and ~36.1%, respectively, and their respective increases in Reactor 2 were more significant at ~62.4% and ~110%. These results agree well with previous studies, which reported that the disturbance imposed on microorganisms induced the release of microbial polymeric substances28,37, in addition to substrate limitations41. When the SRT was increased to 75 d in Phases 4 and 5, the SMP-C and SMP-P concentrations started decreasing, and the decline was more significant with QQ. For instance, in Phase 5, Reactor 2 had the lowest SMP-C and SMP-P levels, at ~54.2% and ~62.4% lower than these respective values in Phase 4. In addition, the longer SRT contributed to decreased SMP levels when comparing Reactor 2 between Phases 2 and 5. This result is consistent with previous studies35,40, which reported that the presence of QQ media reduced soluble biopolymer contents in MBRs. Another previous study also reported that increasing the SRT in MBRs alleviated biofouling42. Notably, QQ caused the more substantive and immediate decrease of SMP-C than that of SMP-P in Phase 4. This could be associated with the inhibition of protease enzyme secretion in the presence of QQ enzymes leading to reduced degradation of soluble protein43. Overall, it can be concluded that the presence of QQ media reduced biopolymer production more significantly when the SRT was extended.Effects of QQ, disturbance, and SRT on mixed liquor characteristics and biological treatment efficienciesMixed liquor characteristics, such as MLSS and floc size, were monitored over time (Fig. 2e, f). The MLSS concentration in both MBRs from Phases 1–3 varied in the range of 2100–2250 mg/L. Disturbance (starvation with shear) at the beginning of Phase 3 caused a slight decrease in biomass concentration compared to that of Phase 2. At the longer SRT (75 d) in Phases 4 and 5, the MLSS concentration increased to 2650–2900 mg/L. It is natural that a longer SRT should increase MLSS levels at the same yield. It appeared that the MLSS levels were a bit higher with QQ than without it as observed from Phase 2 through 5. Microbial growth can be promoted if QS that requires carbon sources is inhibited. A recent finding pointed out that the QQ enzyme (acylase) may increase microbial yield, converting the resource (food) to more biomass44. Operational parameters such as QQ, SRT, and disturbance did not yield significant changes in floc size during the entire study, although fluctuations were possible45. In this study, there was a slight increase in floc size with QQ in Phase 5. The microbial floc size is a function of several factors, such as QS, QQ, nutrients, and operational conditions43. A recent study reported that there was a negative correlation between floc size and EPS level, because the excessive EPS played a role in reducing the hydrophobicity of flocs and, thereby, weakening the cells’ attachment46. It is thus seen that the reduced EPS content may help enhance the floc aggregation, possibly resulting in greater floc sizes.The biological treatment efficiencies of the two MBRs were evaluated in terms of removals of chemical oxygen demand (COD), total organic carbon (TOC), total nitrogen (TN), and total phosphorus (TP) (Supplementary Fig. 3a–d). The effects of SRT and QQ on these removal efficiencies were almost negligible, although disturbance caused a slight decrease in organics removal. The result coincided with the increased SMP level with shear in Phase 3. In short, mixed liquor properties and biological treatment performances seemed to tolerate the effects of QQ and SRT, although they were slightly impacted by disturbance.Microbial community structure changeFigure 3a and Supplementary Table 2 show microbial community variations relative to phases, with clear microbial community structure shifts between phases. Two species were dominant in the seed sludge: Dokdonella immobilis (11.31%) and Sphaerotilus natans (14.91%). After inoculation in the laboratory MBRs, the dominance of these species diminished and other species, being adapted to the synthetic feed, flourished instead (Phase 1). In Phase 2, Thiothrix eikelboomii (15%–18%) and Panacibacter ginsenosidivorans (11%–14%), which were negligible in the seed sludge, became dominant in both reactors. In addition, the relative abundances of Kofleria flava and Flavitalea antarctica increased to 7.57% and 6.95%, respectively. These two species were more abundant in Reactor 1 than Reactor 2. Lastly, the major species of the seed sludge, such as D. immobilis, S. natans, and Terrimonas lutea, became minority species ( 0.7) with SMP-C, SMP-P, and the relative abundance of four individual microbial species (i.e., F. antarctica, P. glucosidilyticus, S. piscinae, and T. carbonis). SMP had a lot stronger correlations with fouling rates than EPS, although the actual amounts of the former were a lot smaller than those of the latter. The result indicates that the soluble biopolymers present in the bulk liquid play a more important role in membrane fouling, possibly due to their direct deposition onto the membrane surface. The strong, negative correlations of fouling rates with COD and TOC removal efficiencies support the above explanation. In particular, P. glucosidilyticus and S. piscinae, which had the highly strong correlations with membrane fouling (r  > 0.87), accordingly exhibited strong correlations with SMP. Notably, T. eikelboomii, which was the most abundant in Reactor 1 of Phases 4 and 5, had a relatively weak negative correlations (r = −0.32) with membrane fouling and so not as strong as did K. flava (r = −0.73). The decrease in the relative abundance of T. eikelboomii in Reactor 2 of Phase 5 should be associated with QQ, but the species might still have been contributing to membrane fouling, as discussed above. As expected, the microbial diversity indices between OTUs and Chao1, as well as Shannon and inverse Simpson, were found to be strongly correlated. However, the fouling rate did not have strong correlations with any of the microbial diversity indices (−0.05 ≤ r ≤ 0.55), although the microbial diversity was always higher in the presence of QQ (see Table 1).Fig. 4: Correlation analysis.Spearman’s correlation coefficients between all the MBR parameters determined in the study.Full size imageOn the other hand, the content of EPS-C and EPS-P had strong, negative correlations with MLSS levels. The biomass increase was accompanied with longer SRT, so the aged sludge produced less EPS amounts leading to the floc size decline (corresponding to a negative correlation, i.e., r = −0.52). Notably, the floc size had a strong, positive correlation (r = 0.84) with TN removal, suggesting that simultaneous nitrification and denitrification possibly occurred with larger biological flocs59. In addition, D. immobilis showed a strong positive correlation (r = 0.84) with TP removal, proposing its role as a potential phosphate uptake strain60. Overall, the relationships between MBR parameters (e.g., fouling rates, mixed liquor characteristics, biological treatment efficiencies, and microbial species dominance) helped better understand the fouling patterns and biological performances in the MBRs with and without QQ.In summary, the QQ effect on MBR antifouling efficacy was clearer when the SRT was extended from 50 to 75 d, although the disturbance (starvation with shear) aggravated membrane fouling, which counteracted the positive QQ effect. QQ yielded a significant biopolymer production decrease with the longer SRT. Accordingly, organic substance removal showed relatively strong, negative correlations with MBR-fouling propensity. MBR microbial communities showed dynamic responses to the feed change, QQ, disturbance, and SRT. With disturbance, F. antarctica, S. piscinae, and P. glucosidilyticus dominated the microbial community leading to substantive membrane fouling. However, the microbial community balance between T. eikelboomii and K. flava, whose relative abundances appeared to be affected by SRT and QQ, played a key role in fouling propensity under stabilized conditions. The correlation analysis showed strong positive relationships between membrane fouling rate and the abundance of several microbial species (F. antarctica, P. glucosidilyticus, S. piscinae, and T. carbonis). However, there was no strong correlation between T. eikelboomii and membrane fouling propensity, possibly due to the antagonism by K. flava, and vice versa. More