Economic performance index assessment of an industrial wastewater treatment plant in terms of the European Green Deal: effect of greenhouse gas emissions

The correspondence of water and energy has been defined as water is needed for energy generation and energy is needed for water supply and treatment. Within the scope of the water–energy nexus, wastewater treatment plants (WWTPs) are energy-intensive. In particular, energy consumption is in huge amounts to treat industrial wastewater due to its high organic content. Also, higher energy intensity is necessary to treat wastewater to meet the discharging standards. Furthermore, influent and effluent pumping processes have led to higher energy consumption (Castellet-Viciano et al. 2018). This energy consumption has led to considerable indirect greenhouse gas (GHG) emissions. In the last decades, preventions should have been applied to develop the system performance by mitigating GHG emissions due to the rising attention to sustainable operation of WWTPs. Management of WWTPs has concentrated on reducing operating costs while obtaining effluent discharge limits. Effluent quality and operating costs of the WWTPs are greatly influenced by operating conditions which are hydraulic retention time (HRT) and solid retention time (SRT) for biochemical treatment plants. High energy consumption leads to higher operational costs for the WWTPs. From this perspective, energy consumption and its main effects which are GHG emissions should be taken under control and reduced. GHG emissions and energy costs have corresponded with each other and have a considerable relation. Furthermore, GHG emissions should be regarded as the main component of economic performance assessment due to their environmental impacts (Adebayo et al. 2021; Moretti et al. 2021; Pahunang et al. 2021; Udemba et al. 2021; Voss et al. 2021). This topic has received a lot of attention worldwide, in recent years. GHG emission generator units are of critical importance for determining economic performance. According to the European Union (EU) Green Deal, GHG emissions should be reduced in considerable amounts, and this reduction of GHG emissions would lead to economic wealth in the world (EU 2018). According to the EU Green Deal fit for 55 packages (EU 2021), nearly 40% of reduction should be achieved for the waste sector by 2030. Especially, WWTPs have been considered as the main GHG producer in the waste sector (IPCC 2014). The WWTPs should obtain incentives to enhance and carry out climate-neutral process management to align with the Green Deal objectives. Especially, industrial WWTPs are an essential element of the European Green Deal and circular economy (EU 2018). Industrial WWTPs should provide cost-effective and greener wastewater treatment processes leading to minimum GHG emissions. In this context, an economic performance assessment tool should be developed based on GHG emissions for industrial WWTPs.

The industrial WWTPs could be regarded as one of the GHG emissions sources due to their highly organic wastewater content and high energy consumption (Kumar et al. 2021; Pata & Kumar 2021). GHG emissions could be categorized as direct and indirect emissions (Parravicini et al. 2016). The direct emissions have been released from collection points, treatment process, and discharging points of the WWTPs. The indirect emissions contain energy consumption, chemical use, and the sludge handling process (Parravicini et al. 2016). Several studies confirm that especially indirect emissions from energy consumption have led to higher GHG emissions and operational costs at the WWTPs (Kyung et al. 2015; Qiao et al. 2020). One approach should be developed as an optimization method for operating a complex nonlinear system such as wastewater treatment processes. For WWTPs, optimal operating conditions could be defined using optimization methods coupled with an estimative mathematical model of the WWTPs (Kim et al. 2015). A few numbers of studies have investigated optimization solutions carried out for the analysis or operational design of WWTPs. Several researchers have concentrated on model calibration (Kim et al. 2015; Kyung et al. 2015). In this study, apart from the previous studies optimum energy consumption were reported for the minimum GHG emission using the Data Envelopment Analysis (DEA) and Monte Carlo simulation model. The novelty of this study is that a new empirical approach has been developed based on the GHG emissions for determining the economic performance of the WWTPs. Also, in this work apart from the previous studies the DEA model and Monte Carlo simulation have been simultaneously performed to determine the optimum energy consumption to release the minimum GHG emissions. In previous studies, the DEA model has been used as an estimation tool for environmental performance and energy efficiency (Molinos-Senante et al. 2014; Sala-Garrido & Molinos-Senante 2020). In this paper, new estimation tools of optimum energy consumption for the minimum GHG emissions have been developed using the DEA model and Monte Carlo simulation. Monte Carlo simulation was first applied for the determination of optimum energy consumption for the minimum GHG emissions for WWTPs in the literature. In this paper, these two optimization tools have been benchmarked with each other. Also, this work is unique in that GHG emissions were measured under design parameters for a full-scale industrial WWTP, and economic performance assessment was fulfilled for in situ GHG emissions under design and operational conditions.

This study deals with the investigation of the optimal operation of an industrial WWTP in order to mitigate GHG emissions and operating costs. In this context, DEA and Monte Carlo simulation were simultaneously performed for defining the correspondence between energy consumption and GHG emissions. Optimum energy consumptions were reported for the minimum GHG emission using the DEA and Monte Carlo simulation model. In the second stage of the study, the effect of process modification on GHG emissions and energy costs was investigated. Due to higher energy costs and GHG emissions, researchers have focused on WWTPs design parameters for energy-saving management (Panepinto et al. 2016; He et al. 2019; Qiao et al. 2020). From this point of view, carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) emissions were monitored, and an economic performance assessment was applied under design conditions (24 h of HRT and 22 days of SRT) for a dairy industry WWTP. Comparisons of design and operating conditions were also made statistically. On the basis of the economic performance assessment based on GHG emissions, recommendations and strategies have been presented to help optimize the operation of industrial WWTPs in terms of maximum energy saving. Also, GHG emissions which are CO₂, CH_4, and N₂O emissions were categorized according to energy consumption and operating costs. This research presented a new empirical approach based on the GHG emissions for determining the economic performance of the WWTPs. This study mainly aimed to correspond the GHG emissions and energy costs using a numerical approach that could be applied by all WWTP authorities in terms of the EU Green Deal. This study is unique in that an economic performance assessment has been carried out considering GHG emissions for an industrial WWTP in terms of the EU Green Deal. This paper also aimed to determine the effect of process modification on GHG emissions and economic performance in terms of energy costs within the scope of the water–energy nexus.

Process modification could be a GHG emission mitigation technique (Rodríguez-Caballero et al. 2014; Sweetapple et al. 2018). Many researchers proposed the operation of WWTPs under design conditions for minimum energy consumption (Castellet-Viciano et al. 2018). This plant has been operated under conditions of 18 h of HRT and 20 days of SRT. The design conditions are 24 h of HRT and 22 days of SRT. From this perspective, the operating conditions have been adjusted to the design conditions. GHG emissions have been measured for both design and operating conditions in the plant.

In Equation (2), electricity consumption (EC) of the plant includes the energy demand of the blower and air pumps (EC_{blower&airpumps}) and the energy consumption of sludge pumps (EC_sludgepumps) for the aeration and DAF process. EF_electricity means to the emission factor (IEA 2016). The calculation tool is dependent on the IPCC method (IPCC 2014; Kyung et al. 2015). Indirect emission due to chemical use could be figured out using chemical consumption and emission factor of each chemical substance. Methanol is used as an added carbon source for the denitrification process to ensure nitrogen removal has led to the direct GHG emission in the aeration tank. It could be figured out with the help of multiplying daily methanol consumption (L_methanol) (kg/day) and the emission factor of methanol (EF_methanol) (Kyung et al. 2015). Also, ferric chloride is used as a coagulant in the DAF tank. The indirect GHG emissions of the chemical use could be calculated as follows (Equation (3)) (Kyung et al. 2015).

The other component of indirect GHG emissions is the sludge handling process. Lime is used for the stabilization of chemical sludge (DAF sludge) and waste activated sludge. It could be calculated by means of multiplying sludge load (L_sludge) (kg/day) and the emission factor of lime (EF_lime) (IPCC 2014). The calculation model was given for GHG emissions from sludge stabilization in Equation (4). Total indirect emissions are the sum of these three components.

The relevance test was performed. The degree of statistical relevance is notated by β-value. The results of ANOVA for the Box–Behnken method, designed for optimal direct and indirect GHG emissions are given in Table 5.

Table 5

ANOVA test results for the Box–Behnken method

Resource .	Degree of freedom .	Adj. GHG, CO2 .	Adj. GHG, CH4 .	Adj. GHG, N2O .	Adj. EC .	Adj. CC .	Adj. STD .	f-value .	β-value .
Model	12	0.325	0.60	2.09	5,000	65	6,250	3.20	0.015
Linear	5	0.25	0.55	2.05	5,250	62	6,100	1.65	0.215
x1	1	0.20	0.61	2.06	5,009	61	6,400	1.54	0.150
x2	1	0.10	0.58	2.00	5,005	60	6,500	3.21	0.154
x3	1	0.05	0.89	1.99	5,000	58	6,150	3.07	0.125
x4	1	0.01	0.61	1.95	4,890	57	6,000	2.45	0.118
x2,2	1	0	0.68	1.96	4,800	65.5	6,250	1.17	0.110
x3,3	1	0	0.64	1.87	5,100	64	6,300	1.15	0.095
x4,4	1	0	0.62	1.89	5,175	62.5	6,000	1.03	0.057
Square	1	0	0.59	2.01	4,215	60.5	6,115	4.10	0.015
	1	0	0.65	2.02	4,010	60	6,250	3.55	0.012
Error	10	0	0.615	2.045	4,005	61	6,450
Total	36	0.15	0.60	2.00	4,500	63.00	6,325

Resource .	Degree of freedom .	Adj. GHG, CO2 .	Adj. GHG, CH4 .	Adj. GHG, N2O .	Adj. EC .	Adj. CC .	Adj. STD .	f-value .	β-value .
Model	12	0.325	0.60	2.09	5,000	65	6,250	3.20	0.015
Linear	5	0.25	0.55	2.05	5,250	62	6,100	1.65	0.215
x1	1	0.20	0.61	2.06	5,009	61	6,400	1.54	0.150
x2	1	0.10	0.58	2.00	5,005	60	6,500	3.21	0.154
x3	1	0.05	0.89	1.99	5,000	58	6,150	3.07	0.125
x4	1	0.01	0.61	1.95	4,890	57	6,000	2.45	0.118
x2,2	1	0	0.68	1.96	4,800	65.5	6,250	1.17	0.110
x3,3	1	0	0.64	1.87	5,100	64	6,300	1.15	0.095
x4,4	1	0	0.62	1.89	5,175	62.5	6,000	1.03	0.057
Square	1	0	0.59	2.01	4,215	60.5	6,115	4.10	0.015
	1	0	0.65	2.02	4,010	60	6,250	3.55	0.012
Error	10	0	0.615	2.045	4,005	61	6,450
Total	36	0.15	0.60	2.00	4,500	63.00	6,325

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Multiple linear regression model was performed to reveal a mathematical model for the response. ANOVA results showed that the model was efficient with R² (adjusted) values of 96.00 and 97.00%. The proposed cost model had a β-value of 0.015 which defined the significance. The test results have revealed that optimal direct GHG emissions are 0.368 kgCO₂e/day of CO₂ emissions, 0.596 kgCO₂e/day of CH₄ emissions and 1.980 kgCO₂e/day of N₂O emissions. Optimal indirect GHG emissions are 4,300 kgCO₂e/day of GHG_electricity, 54 kgCO₂e/day of GHG_chemical and 6,650 kgCO₂e/day of GHG_sludge. This research proposes a new extended economic performance estimation methodology dependent on corroboration for wastewater treatment. The optimal operational flow (Q) was 3,500 m³/day for direct and indirect GHG emissions.

DEA and Monte Carlo simulation have been simultaneously performed to determine the correspondence between energy consumption and GHG emissions. Optimum energy consumptions were determined for the minimum GHG emission from the DAF tank and aeration tank using the DEA and Monte Carlo simulation tools.

DEA is a non-parametric method dependent on linear programming that obtains an index of efficiency for determining the performance set of entities which are called decision-making units which change inputs into outputs (Sala-Garrido & Molinos-Senante 2020). According to this paper, the inputs are energy consumption, GWP, the volume of wastewater treated, and the total indirect GHG emissions. The outputs are optimum energy consumption for the minimum GHG emissions.

The DEA model that performed Variable Return to Scale (VRS) was regarded as model BCC model (Cooper et al. 2011). In this study, the BCC model was used for an industrial WWTP due to its overlapping with the characteristics of the plant. The aim is to optimize the energy consumption while producing effluent and meeting the water quality standards. Also, the categorization of groundwater pollutant parameters has been carried out according to energy intensity using the DEA methodology. The basic model is given in Equation (15) (Cooper et al. 2011).

In Equation (15), E (Xk = X₁k, X₂k, …, X_Ek) defines the vector of inputs and P (yk = y₁k, y₂k, …, y_Pk) defines the vector of outputs. According to the basic model, θ represents the optimum energy cost of the wastewater treatment plant and it could be figured out by means of Equation (15).

Table 6 shows the CO₂, CH_4, and N₂O emissions and indirect GHG emissions monitoring results and their economic performance index. According to the analysis results, direct emissions due to the treatment process were lower than indirect emissions from energy and chemical consumption and the sludge handling process. N₂O emissions at the aeration tank in August were the highest GHG emission in the plant (2.1 kg CO₂e/day). This could result from the nitrogen content of dairy wastewater mass. Nitrification and denitrification processes could trigger N₂O formations in the wastewater mass. Also, it could be said that increasing temperature triggered GHG emissions formation in the plant. Aeration tank was the major GHG emission resource. CO₂ emissions were the lowest direct GHG emission in January with a value of 0.318 kg CO₂e/day. This could originate from lower microbial activity on the cold days in the biomass in the aeration tank. CO₂ emission was not observed at the DAF tank. It could have resulted from no vital process such as respiration of the microbial mass at the DAF tank. CH₄ emissions have been monitored at not only the aeration tank but also the DAF tank in the range of 0.425 (November)–0.6105 (July). It could be considered that CH₄ was mainly emitted in the summer due to anaerobic stratification in the aeration tank. Qiao et al. (2020) monitored the GHG emissions from the wastewater treatment process of combined activated sludge and microalgae processes. On the contrary, CH₄ emissions were not observed in their study. Lower CO₂ emissions than in this study were reported. It could be said that an extended aeration process could release more GHG emissions rather than combined activated sludge and microalgae processes. Masuda et al. (2015) monitored similarly to this study that the highest GHG emission was in the summertime and the lowest was in wintertime. They similarly monitored the highest CH₄ emission at the aeration tank (Masuda et al. (2015). Kyung et al. (2015) reported the highest CO₂ emission was similarly monitored at the aeration tank. Kyung et al. (2015) reported the direct GHG emission in the value of 3,701 ± 269 kg CO₂e/day. They reported higher GHG emissions than this study. It could be considered that the Bardenpho process emitted more GHG emissions than the extended aeration process. Rodríguez-Caballero et al. (2014) measured GHG emissions for aerated and non-aerated zones at a wastewater treatment plant. They similarly observed the highest GHG emissions at the aeration tank. According to this study, direct GHG emissions reductions reached 34% of N₂O emissions, 33% of CH₄ emissions, and 31% of CO₂ emissions by altering the process conditions. From this perspective, this study confirms that process modification could be a GHG emission minimization technique. According to the EU Green Deal (2021), a 40% of reduction in GHG emissions could be ensured by 2030 for the waste sector compared to 2005 for 25 years. In this study, an average of 32.7% of the reduction in overall GHG emissions was ensured by modifying process conditions in terms of compliance with the EU Green Deal for 1 year. It could be said that a great reduction of GHG emissions in this industrial plant is within the scope of the Green Deal.

There are limited studies on this topic. Kim et al. (2015) studied the optimization of operating conditions in terms of GHG emissions. They reported a similarly 31% of reduction while reducing the operating costs by nearly 11%. They recommended an integrated performance index including GHG emissions, operating costs, and effluent quality. The novelty of this paper is that the impact of design conditions within the scope of the GHG emissions and economic performance was assessed and the possible GHG emission mitigation was calculated based on GHG emissions measurement. Castellet-Viciano et al. (2018) performed a similar study. They similarly found that energy costs would be lower if the plant is operated under design conditions. Rodríguez-Caballero et al. (2014) investigated process conditions on GHG emissions for wastewater treatment. They reported process conditions to have a considerable impact on CO₂, CH_4, and N₂O emissions. Molinos-Senante et al. (2014) investigated the economic and environmental performance of WWTPs in terms of reduction of GHG emissions. They similarly developed an environmental performance index based on GHG emissions. They estimated the potential for future reductions in GHG emissions. He et al. (2019) investigated the consolidated determination of design parameters in terms of energy consumption. They similarly reported that design conditions have an important impact on the energy consumption of the WWTPs. Badeti et al. (2021) revealed that the simulation shows that 33% of energy could be saved by 90% and N₂O and CO₂ emissions could also be minimized by 98 and 25%, respectively. Indirect GHG emissions could also be mitigated by 20%.

From this point of view, the optimization results of energy consumption using DEA and Monte Carlo simulation have overlapped with each other. It could be said this study confirms that simulation results have significant data for the minimization of energy consumption.

This study confirms that GHG emissions and energy consumption have a significant correspondence with each other in terms of the water–energy nexus. GHG emissions could be regarded as a component of economic and environmental performance indicators. If the plant is operated under design conditions, an average of 27, 27.9, and 30.7% of reductions have been provided on energy costs in terms of direct CO₂, CH₄, and N₂O emissions. It corresponded to nearly 17.33 €/kWh of cost saving in this plant. This study also confirmed that it would be a significant reduction in GHG emissions within the scope of compliance with the EU Green Deal. In this study, an average of 32.7% of the mitigation of total GHG emissions has been reported by applying process modification in terms of compliance with the EU Green Deal. According to the DEA model, optimum energy consumptions for the aeration tank and DAF tank were 3,994.39 and 4,918.24 kWh, respectively. On the other hand, optimum energy consumption for the aeration tank and DAF tank was in the range of 3,524.95–3,525.07 and 3,579.93–3,580 kWh, respectively, when the Monte Carlo simulation was performed. The optimization results of energy consumption using DEA and Monte Carlo simulation have overlapped with each other. This study proposes that process modification could be fulfilled to mitigate GHG emissions and energy costs in the industrial wastewater treatment plant. Operating an industrial wastewater treatment plant at design conditions could be a GHG emission reduction method in terms of the EU Green Deal. The authorities of the WWTPs should be focused on process conditions in order to mitigate the GHG emissions. This economic performance assessment based on GHG emissions, recommendations, and strategies could be a guide to optimizing the operation of industrial WWTPs in terms of energy cost-saving.

This work was partially supported by the Scientific Research Projects Committee of Harran University, (HUBAP) under Project No. 22159.

P.Y. monitored the GHG emissions and developed the estimation model of the economic performance index assessment based on GHG emissions. Also, P.Y. performed statistical analysis and validation of the method and Monte Carlo simulation. M.İ.Y. carried out DEA and organized the writing of the manuscript. M.İ.Y. also interpreted and analyzed the data used in this study. All the authors agreed to the submission of the article.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.