Mathematical Modeling and Indirect Carbon Emission Reduction Analysis of Urban Wastewater Treatment Systems Under Different Temperature Conditions

Abstract

In the context of achieving the two-carbon target, this study utilized a wastewater treatment plant in Shenyang City as a case study to accurately calculate indirect emissions related to energy and chemical consumption within the energy-intensive wastewater treatment industry. Sumo software was employed for precise mathematical modeling. Considering the operational characteristics of wastewater treatment plants in cold regions, this study innovatively divided the annual operation cycle into two periods, namely normal temperature and low temperature, and determined the optimal operational parameters under a low-carbon mode. The results indicate that precise regulation of dissolved oxygen concentration to 0.5–1.5 mg/L (normal temperature period) and 1–2 mg/L (low temperature period) can significantly reduce carbon emissions related to electricity consumption by 13,781.9 t CO₂-eq. From the perspective of chemical consumption, adjusting the dosage of polyaluminum chloride (PAC) to 75% and sodium acetate to 70% during the normal temperature period can lead to a reduction in indirect carbon emissions of 1614.4 t CO₂-eq compared to the same period last year. During the low-temperature period, by reducing the dosage of polyaluminum chloride to 80% and sodium acetate to 75%, the indirect carbon emissions can be reduced by 1557.3 t CO₂-eq compared to the corresponding period last year. After optimization, USD 1.49 million can be saved. This study simulated the operation conditions of cold-region urban wastewater treatment plants at different times to effectively control carbon emissions resulting from energy and chemical consumption in wastewater treatment. This result can provide innovative ideas for energy saving and carbon reduction in cold-region wastewater treatment plants.

1. Introduction

The frequency and intensity of extreme climate events worldwide are showing a clear upward trend with the intensification of global warming [1]. In order to curb the warming trend and reduce climate risks, the United Nations Framework Convention on Climate Change (UNFCCC), Kyoto Protocol, and Paris Agreement have been adopted worldwide. Under the Paris Agreement, the shared goal of the world is to keep the average temperature rise below 1.5 °C above preindustrial levels [2]. The goal is based on deep insights into the science of climate change and the urgent need to avert its worst consequences. The fifth report of the United Nations Intergovernmental Panel on Climate Change (IPCC) has made it clear that human influence on the climate system, as well as human use of fossil fuels and land-use change, are the main reasons for the rise in greenhouse gas (GHG) concentration [3]. China’s commitment to peaking carbon emissions by 2030 and achieving carbon neutrality by 2060 underscores its determination to reach carbon reduction targets within a relatively short time [4].

The wastewater treatment processes exert a discernible influence on global warming and climate change [5]. As an energy-intensive industry, the wastewater treatment sector accounts for approximately 1.6% of global carbon emissions [6], with its GHG emissions primarily consisting of CO₂, CH₄, and N₂O [7]. The carbon emissions from wastewater treatment processes are categorized into two groups: direct emissions and indirect emissions. Direct emissions primarily refer to the greenhouse gas emissions directly generated due to biochemical reactions in the wastewater treatment process. They are influenced by various factors, including wastewater quality; the wastewater treatment process, operating conditions, and equipment efficiency; and the internal reflux ratio. Indirect emissions mainly arise from energy consumption and chemical usage. In terms of energy consumption, electricity consumption is identified as the primary source of indirect emissions [8]. Wastewater treatment plants necessitate substantial electricity consumption for the operation of various equipment, including pumping stations, fans, and agitators. Moreover, the utilization of chemical agents such as disinfectants and flocculants also results in indirect emissions. To mitigate these emissions, it is worth exploring alternative agents with low energy consumption and minimal emissions. Additionally, online monitoring technology can be employed to dynamically adjust agent dosages to prevent excessive usage. Furthermore, enhancing electricity efficiency and adopting renewable energy sources represent effective approaches for reducing carbon emissions. By refining agent management practices and optimizing power usage, significant reductions in GHG emissions from the wastewater treatment process can be achieved.

Mathematical models play a central role in the field of wastewater treatment by simplifying complex biochemical reactions into mathematical expressions [9]. They also facilitate the optimization of process parameters and enable the prediction of effluent quality during a specific process [10]. The activated sludge process was pioneered by Edward Ardern in 1914. From the simplified models proposed by researchers from Eckenfelder [11] and McKinney [12] to Lawrence et al. [13], it effectively fulfills the requirements for steady-state wastewater treatment. The International Association on Water Quality (IAWQ) introduced a series of activated sludge models, including ASM1, ASM2, ASM2d, and ASM3. ASM3 models have significantly enhanced our comprehension of the activated sludge process, particularly in simulating biological phosphorus removal, chemical phosphorus removal, denitrification, and phosphorus accumulation bacteria [14].

In tandem with advancements in computational technology and the profound exploration of microbial kinetics, commercial simulation software anchored in ASM series mathematical models, such as GPS-X [15,16,17,18], BioWin [19,20,21], SUMO [22,23,24], WEST [25,26,27], and EFOR [28], have surfaced to meet contemporary demands. These software tools signify the evolution of wastewater treatment modeling from its foundational theoretical underpinnings to practical engineering applications. The maturation of wastewater treatment simulation modeling software underscores a pivotal shift from academic inquiry to industrial utility, solidifying its role as a critical instrument in wastewater engineering design, operational refinement, and managerial strategy formulation. In regions such as North America, Europe, and Australia, modeling software has gained widespread acceptance in ensuring high water quality through its utilization in plant design, analysis, control, prediction, and optimization. These tools play a crucial role in the optimal design, operation, and transformation of wastewater treatment plants; they assist engineers and decision makers in making more accurate management decisions. The urban wastewater treatment process is a complex biochemical reaction process. By establishing an urban wastewater treatment model, it becomes possible to simulate the relationship between inlet and outlet water components, enabling a deeper understanding of studied phenomena and laws. Additionally, this model can provide real-time predictions for the effluent index of urban wastewater treatment and analyze the influence of process variables on effluent quality to mitigate environmental pollution caused by excessive discharge.

Based on the current situation of insufficient research on the theory and practice of the collaborative development of pollution reduction and carbon reduction in sewage treatment plants, this study expects to reduce the greenhouse gases caused by sewage treatment plants in China through the simulation of sewage treatment plants, in particular, greenhouse gas emissions in the cold northeast area of China under conservative operations such as excessive aeration and excessive dosing of agents to ensure drainage standards in winter. Through the simulation of Sumo 22 software, this research innovatively divides the annual operation cycle into two stages—the normal-temperature period and the low-temperature period—based on the characteristics of Northeast China’s cold region and annual temperature changes. The study thus explored the impact of different operational parameters on carbon emissions from sewage treatment plants, aiming to achieve pollution reduction and carbon emission reduction goals.

2. Materials and Methods

2.1. Introduction to Sumo

In this study, the open-source sewage treatment process simulation software SUMO, developed by Dynamita in France, was employed. The transparent codebase empowers users with a profound comprehension of parameter configuration and simulation mechanics. When faced with abnormal results, the basic assumption based on Sumo is that users can diagnose the source and quickly implement targeted correction measures, which is widely used [29,30]. The Sumo software model library is extensive and encompasses not only the traditional ASM activated sludge series model but also unique features of Sumo, enabling a detailed description of the operation of the wastewater treatment plant. For instance, Mini Sumo estimates sludge and oxygen demand, and Sumo1 is employed for biological phosphorus removal, one-step nitrification, one-step denitrification, and anaerobic digestion. Additionally, Sumo2 is used for two-step nitrification and denitrification, while Sumo4N predicts greenhouse gas emissions. The Sumo process unit covers almost all commonly used processing processes. In this study, Sumo software was used to simulate a wastewater treatment plant in Shenyang City to analyze its emission reduction path.

2.2. Modeling of Sewage Treatment Plant

In this paper, Sumo software is used to establish a simple, improved anaerobic–anoxic–oxic (AAO) model for the sewage treatment plant located in the west of Shenyang, Liaoning province. The modeling process includes the establishment of a process flow chart, the selection of the model, the setting of water intake parameters, and the setting of operation parameters of the sewage treatment plant. The process flow of a sewage treatment plant in Shenyang is shown in Figure 1.

2.3. Model Running Parameter Setting

In order to enhance the accuracy, reliability, and scientific rigor of the simulation operation for the wastewater treatment plant model established by Sumo software, we divided the steady-state simulation into two stages: the normal-temperature period and low-temperature period. The demarcation point was set at 14 °C, which represents the optimal growth temperature for activated sludge [31]. The normal-temperature period spans from May to October, while the low-temperature period encompasses January to April and November to December. The water intake parameters and default operating parameters of the wastewater treatment plant are based on comprehensive data collected throughout 2023, with key parameters presented in

Table 1. Mean parameters of influent water quality for the whole year 2023.

Project	Normal Temperature Period Data	Low Temperature Period Data
Flow Rate (Q) (m3/d)	127,558.24	118,720.82
Total Kjeldahl Nitrogen (TKN) (mg/L)	30.79	32.91
Total Phosphorus (TP) (mg/L)	3.36	3.15
Pondus Hydrogenii (pH)	7.25	7.24
Chemical Oxygen Demand (COD) (mg/L)	154.11	158.22
NH3-N (mg/L)	21.71	20.86
Total Suspended Solids (TSS) (mg/L)	84.00	67.93
Temperature (T) (°C)	18.00	10.00

.

If Sumo software is utilized for preliminary simulations with default values, it will yield a considerably high relative error. Hence, an extensive sensitivity analysis and comparison of the kinetic and chemical parameters of the established model were conducted to refine the model’s realism in reflecting actual water plant operations. Sensitivity analysis was performed on the parameters within Sumo software by subjecting influent component parameters to a 10% increase to assess their impact on effluent quality indices. Subsequently, sensitivity coefficients were calculated using Formula (1). A higher sensitivity coefficient indicates significant influence of a parameter on output results, while a coefficient close to zero suggests insensitivity of output towards that specific parameter [32,33].

S_ij = |∆y_i/y_i × x_i/∆x_i|

(1)

where S_ij is the sensitivity coefficient, ∆y_i is the change of effluent water quality index before and after changing the relevant parameters of inlet water quality, y_i is the effluent water quality index simulated for the first time, x_i is influent water quality-related parameters, and ∆x_i is variation of inlet water quality-related parameters.

2.4. Carbon Emission Accounting of Wastewater Treatment Plants

GHG emissions from wastewater treatment plants are classified into direct and indirect emissions [34]. Direct emissions refer to the non-biological GHG emissions that occur directly in the wastewater and sludge treatment process [35], while indirect emissions refer to the GHG emissions generated by the energy (such as electricity) and chemicals used in the wastewater and sludge treatment process. This study focused on accounting for and optimizing the indirect emissions of a wastewater treatment plant in Shenyang. The accounting method refers to two guidelines, namely the “IPCC 2006 National Greenhouse Gas Inventory Guide 2019 Revised Edition” and the “Urban Water System Carbon Accounting and Emission Reduction Path Technical Guide”, for carbon emission accounting of different objects.

Carbon emissions and carbon emission intensity can be converted to each other, and the calculation is as follows [36,37]:

CE = CES × Q × T × 365

(2)

where CE (kg CO₂-eq) is the total carbon emissions during the service period, CES (kg CO₂-eq/m³) is the carbon emission intensity during the service life, Q (m³/d) is the average daily treatment water volume per wastewater treatment plant to meet the standard water volume, and T(a) is the length of service.

Formulae (3) and (4) demonstrate the computation of carbon emissions resulting from electric energy consumption during the wastewater treatment process [36,37]:

CES_d = (E_d × EF_d)/Q

(3)

where CES_d (kg CO₂-eq/m³) is the carbon emission intensity from consumption of purchased electricity, E_{d (KW h/a)} evaluates the total electricity consumption during the year, and EF_d (kg CO₂-eq/KW h) is the electricity emission factor in the region.

CO_2Emission = W × Se × EF_d

(4)

where CO_2Emission (t/a) is the indirect CO₂ emissions from electricity consumption in wastewater treatment links, W (t/a) is the wastewater treatment capacity, and Se (KWh/t) is the specific energy consumption of the wastewater treatment process.

The calculation of indirect carbon emission caused by agent consumption is shown in Equation (5) [36,37]:

CEScl = ∑ⁿ_i = 1(Mcl × EFcl,i)/Q

(5)

where CEScl (kg CO₂-eq/m³) is the indirect carbon emission intensity from the consumed agent, Mcl (kg/a) evaluates the total consumption of chemicals i in the year, EFcl,i (kg CO₂-eq/kg) is the emission factor of agent I, and n is the use of n agents in total.

3. Results and Discussion

3.1. Results of Sensitivity Analysis

Sensitivity coefficients were calculated based on the annual average data of a wastewater treatment plant in Shenyang City to establish a preliminary model. For the influence of different temperature conditions on biomass, biological activity, and other factors of the improved AAO process, the model needed to be corrected. The parameters with higher sensitivity coefficients for COD, biochemical oxygen demand (BOD), TN, and TP are shown in

Table 2. Sensitivity analysis results of key parameters.

Parameters	Default	Si,j				Simulated Value at Normal Temperature	Simulated Value at Low Temperature
		COD	BOD	TN	TP
Maximum specific growth rate of OHOS	4	0.80	0.88	1.70	1.26	1	1
Decay rate of OHOS	0.62	1.24	1.43	1.68	1.16	0.68	0.7
Fermentation growth rate of PAOS	0.45	0.00	0.00	2.00	0.06	0.1	0.1
Maximum specific growth rate of PAOS under P limited	0.49	0.94	0.33	1.69	0.64	0.1	0.1
Decay rate of ABOS	0.17	0.76	2.00	1.75	0.86	0.25	0.12
Half-saturation of O2 for ABOS(AS)	0.25	0.98	0.75	1.68	0.83	0.23	0.1
Yield of OHOS on readily biodegradable substrate under aerobic conditions	0.67	0.01	0.05	1.09	0.35	0.66	1
Yield of OHOS on readily biodegradable substrate under anoxic conditions	0.54	0.06	0.56	2.86	0.43	0.60	0.83
Yield of OHOS on readily biodegradable substrate under anaerobic conditions	0.1	0.01	0.12	0.37	0.64	0.14	0.14
P content of biomasses	0.02	0.00	0.00	0.04	0.18	0.018	0.018
Yield on ultimate BOD	0.95	0.00	1.00	0.00	0.00	0.6	0.3

. There are a total of 11 high-sensitivity parameters, primarily encompassing microbial parameters related to heterotrophic bacteria (OHOS), phosphorus-gathering bacteria (PAOS), and ammonia-oxidizing bacteria (AOBS).

The model parameters were refined through a sensitivity analysis, with particular emphasis on variables exhibiting higher sensitivity coefficients. The parameter adjustments were made based on two aspects: firstly according to the given model parameters and the results of the sensitivity analysis and secondly by considering the actual operational conditions of wastewater treatment facilities and referencing previous research findings [29,38,39]. During the normal-temperature period and low-temperature period, we made adjustments to the model accordingly, as shown in Table 1. Temperature fluctuations have an impact on microbial activity, enzyme activity, metabolic rate, and microbial growth. As the temperature decreases, the growth rates of OHO, AOB, and PAO show a decreasing trend. Specifically during the low-temperature period, there was an increase in the decay rate of OHOS, indicating a slight rise in the mortality rate for aerobic OHO_S due to reduced metabolic efficiency and increased vulnerability to death caused by lower temperatures. Under normal-temperature conditions, the decay rate of ABO_S was adjusted from its default value of 0.17 to 0.25, which reflects their high level of activity in suitable temperature environments, resulting in a corresponding increase in attenuation rate. The variation observed in OHO_S yield across aerobic, anoxic, and anaerobic conditions corresponds to fluctuations in microbial metabolic rates influenced by environmental factors. Yield on ultimate BOD significantly decreased from its default value of 0.95 at low temperatures to 0.3, while at normal temperatures, it decreased slightly less to 0.6. This indicates that the ability of microorganisms to metabolize organic matter is greatly reduced at lower temperatures, leading to a decrease in final BOD production.

The operation of the wastewater treatment plant is significantly influenced by different temperature conditions, as depicted in Figure 2. During periods of low temperature, the effluent levels of COD and BOD are generally higher compared to normal-temperature periods due to reduced microbial activity and dormancy among certain microorganisms, leading to decreased efficiency in wastewater treatment. TN and TP exhibited noticeable fluctuations during the low-temperature period, possibly attributed to inhibited metabolic activities of microorganisms at lower temperatures, particularly denitrifying bacteria whose activity is diminished, thereby affecting nitrogen removal performance within the system. Furthermore, low temperatures may also impact the stability of granular sludge in wastewater treatment systems, which subsequently affects phosphorus removal efficiency. After correction, average relative errors for measured values of COD, BOD, TN, and TP during the low-temperature period were found to be 0.34%, 14.31%, 10.46%, and 5.17%, respectively. The average relative errors for measured values of COD, BOD, TN, and TP were observed as 10.07%, 15.31%, 1.43%, and 15.69%, respectively. The error between simulated values and measured values is approximately around 10%, falling within an acceptable range for error tolerance and indicating that the established model can effectively simulate actual operations of wastewater treatment plants during normal temperature period while accurately reflecting their operational status.

The average inflow and outflow data for the normal-temperature period and low-temperature period in 2023 were selected as the calibration data in this research. The relevant model parameterization was adjusted, and the steady-state simulation calibration of the wastewater treatment plant operation in the warm period and cold period was carried out separately. During steady-state operation, an error of approximately 10% was maintained, indicating a high agreement between simulated and actual water output values. Subsequently, dynamic water intake data from both periods were used to dynamically simulate the corrected SUMO model, with the actual dynamic water output data in 2023 serving as a reference for system verification (Figure 3). The verification results demonstrate that the SUMO model can effectively reflect the actual effluent situation and has potential as a fundamental model for optimizing water plants. During dynamic operation, errors generally ranged from 10% to 40%, which may be attributed to fluctuations in water quality and temperature changes.

3.2. Energy Optimization and Carbon Emission Reduction Analysis

In consideration of the fact that wastewater treatment plants are intricate and multifaceted systems, where the accuracy of dissolved oxygen control directly impacts the efficiency of wastewater treatment as a primary factor influencing the biological treatment process, it is noteworthy to mention that indirect carbon emissions from wastewater treatment plants predominantly arise from energy consumption and chemical usage. Based on conducted surveys, energy consumption in Chinese wastewater treatment plants accounts for approximately 1% of national electricity consumption, while roughly 100,000 tons of various chemicals are consumed annually [40]. Among all wastewater treatment processes, the aeration process accounts for the highest energy consumption, ranging from 40% to 75% of the total energy demand [41]. The fluctuation of DO concentration directly impacts the energy consumption of the aeration system, and a high DO concentration will significantly escalate energy consumption and operational expenses. Moreover, the concentration of dissolved oxygen can directly or indirectly influence the bioconversion rate of pharmaceuticals in the wastewater treatment process [42]. The maintenance of an appropriate DO level was demonstrated to be a crucial prerequisite for the reduction of N₂O emissions during the processes of nitrification and denitrification [43]. Several studies have provided support for the notion that controlling DO concentration can lead to reduced energy consumption. For example, Piotrowski et al. [44] developed a hierarchical nonlinear adaptive control system for a biological sequence batch reactor (SBR), which achieved significant reductions in energy consumption through precise regulation of DO concentration. Abulimiti et al. [30] employed an ultra-low oxygen strategy combined with a high-frequency control approach to effectively manage DO concentration and decrease N₂O emissions.

This study employed the Sumo software to regulate the DO concentration, implementing a differentiated control strategy informed by seasonal characteristics to enhance wastewater treatment efficiency. As shown in Figure 4, during the low-temperature period, taking into account the inhibitory effects of water temperature on biological activity, the DO concentration range was established based on variations in outflow data for each treatment process, ultimately set between 1 and 2 mg/L to maintain an equilibrium between microbial activity and treatment efficacy. Conversely, during the normal-temperature period, the DO concentration was determined through a comprehensive analysis of outflow data, ultimately fixed between 0.5 and 1.5 mg/L.

The annual power consumption of this wastewater treatment plant during operation amounts to 19,349,250 kWh. The average emission factor for the Northeast China power grid is 1.0631 kg CO₂-eq/kWh, resulting in carbon emissions of 205,700 t CO₂-eq from power consumption during plant operation. In Northeast China, seasonal variations and temperature fluctuations exert a significant influence on electricity usage in wastewater treatment plants [45], with a notable increase observed under low-temperature periods. Figure 5 illustrates that peak power demand occurs during the cold month of January at this facility. Previous research indicates that approximately 50% to 60% of the total energy consumption in conventional activated sludge wastewater treatment plants is attributed to the aeration process [46,47]. The optimization of DO by Sumo significantly reduces power consumption and its associated carbon emissions, leading to a reduction of 13,781.9 t CO₂-eq in carbon emissions resulting from power usage alongside savings of USD 750,000 in electrical expenses.

The aeration process can be enhanced by employing advanced equipment or adopting micro-bubble aeration, thereby optimizing the efficiency of aeration [48]. With the integration of an online monitoring instrument, real-time tracking of DO and pollutant concentration is achieved, enabling accurate control of aeration through digital simulation to prevent energy waste caused by excessive aeration. Relevant studies have demonstrated that the enhanced-precision aeration system can lead to reductions in power consumption and carbon emissions from wastewater ranging from 2.8% to 6.33% and 10.37% to 17.24%, respectively. Furthermore, augmenting the air flow rate in the aeration tank significantly decreases energy consumption [49,50].

3.3. Agent Optimization and Carbon Emission Reduction Analysis

The activated sludge process, which is the predominant method employed in wastewater treatment plants in China, necessitates energy and agents to supply dissolved oxygen and a suitable habitat for aeration and microbial flora within the biological pool. This facilitates complete contact between organic matter present in influent wastewater and microorganisms residing in the pool, leading to effective removal of excessive organic matter from the water. However, excessive utilization of agents not only substantially escalates costs but also exerts profound influence on treatment efficiency and economic benefits. This study aimed to investigate the impact of polyaluminum chloride (PAC) dosage and sodium acetate on effluent quality while considering how seasonal variations affect agent requirements and treatment effectiveness, thereby comprehensively elucidating optimal strategies for wastewater treatment under diverse climatic conditions.

The dosage of PAC in the dosing unit was simulated and analyzed under conditions that ensure unaffected effluent quality, with the original dosage considered as the upper limit for preliminary analysis. From

Table 3. TP effluent values at different PAC dosages.

TP Effluent Value (mg/L)	Different PAC Dosage (%)
	60	65	70	75	80	85	90	95	100
Normal-temperature period	0.32	0.31	0.31	0.30	0.30	0.30	0.30	0.30	0.30
Low-temperature period	0.29	0.28	0.27	0.27	0.26	0.26	0.26	0.26	0.26

, it can be observed that even when reducing the PAC dosage to 80% of its original amount during the low-temperature period, it still complies with the TP effluent standard of the water plant. Similarly, a PAC dosage of 75% of the original also meets the TP effluent standard during the normal-temperature period. Temperature significantly impacts PAC’s efficiency in TP removal. Within a certain range, lower temperatures result in diminished effectiveness of PAC in removing TP due to their influence on non-soluble total phosphorus (NR-TP) removal. Under low-temperature conditions, there is an increase in residual non-dissolved total phosphorus after treatment, which hampers effective removal and limits further increases in PAC dosage for reducing residual content.

Due to the evident carbon source shortage for denitrification in this study, the continuous addition of a stable and safe carbon source is necessary. Sodium acetate was selected as the optimal choice in this project, with its dosage optimized using Sumo software. The results indicate that while ensuring effluent quality, sodium acetate dosage can be reduced by 75% during low-temperature periods and further reduced to 70% during normal-temperature periods, effectively meeting wastewater treatment requirements.

The annual consumption of PAC during the operation of the wastewater treatment plant amounted to 5829.06 t, while sodium acetate accounted for an annual consumption of 3710.43 t. Based on the literature reports, the emission factor for PAC is reported as 1.61 kg CO₂-eq per kilogram [51], and the emission factor of sodium acetate is 1.07. Figure 6 shows the detailed analysis of the monthly fluctuation of agent consumption and carbon emission. The usage of the two agents in the low-temperature period was significantly higher than that in the normal-temperature period. Due to the influence of water temperature on chemical reactions and biological activities, the activity of microorganisms decreases under low-temperature conditions, so it is necessary to increase agents to compensate for biological treatment. The peak carbon emission of PAC was 1920 t CO₂-eq in November, and the carbon emission of sodium acetate in each month was relatively average. The average emission of sodium acetate in other months was 300 t CO₂-eq after September and October, with higher emissions, were removed.

The dosage of the agent was optimized using Sumo software. Under the condition of meeting the effluent standards of the water plant, Figure 6 shows the emissions of PAC and sodium acetate before and after optimization. During the normal-temperature period, carbon emissions from PAC were reduced by 991.3 t CO₂-eq, and carbon emissions from sodium acetate were reduced by 623.1 t CO₂-eq. In the low-temperature period, carbon emissions from PAC were reduced by 1084.0 t CO₂-eq, carbon emissions from sodium acetate were reduced by 473.3 t CO₂-eq, and annual carbon emissions were reduced by 1096.4 t CO₂-eq. After optimization, the yearly expenditure on PAC was slashed by a noteworthy 1289 t, paralleled by a diminution in sodium acetate utilization by 1024.7 t, cumulatively sparing USD 740,000 in dosage of the agent.

The aforementioned measures enable wastewater treatment plants in cold regions to effectively reduce energy and chemical consumption while ensuring optimal treatment outcomes. By implementing these strategies, wastewater treatment facilities in cold areas can not only ensure efficient water quality treatment but also significantly minimize energy and chemical consumption, thereby achieving the dual objectives of energy conservation and emission reduction, thus contributing to the sustainable development of the environment.

4. Conclusions

This study focused on saving energy and reducing consumption at a wastewater treatment plant in the cold area of Northeast China. Sumo software was used to establish a model under different influent temperature conditions to calculate and analyze energy and chemical consumption. Based on model parameter settings, sensitivity analysis results, and actual operational data from wastewater treatment plants, 11 parameters with high sensitivity were subjected to repeated calibration and optimization. After calibration, the relative error of COD, BOD, TN, and TP of the effluent in the low-temperature period and normal-temperature period was about 10%.

The calculation results show that the carbon emission generated by electricity consumption was 205,700 t CO₂-eq, while the carbon emissions from chemical usage totaled 13,354.94 t CO₂-eq. After optimization with Sumo, the dissolved oxygen concentration in the wastewater treatment plant can be reduced from 6.51 mg/L to 0.5–1.5 mg/L during the normal-temperature period, with a reduction of 615.68 t in PAC dosage and 582.34 t in sodium acetate dosage, leading to a decrease of 1614.4 t CO₂-eq in indirect carbon emissions from the chemicals used. During the low-temperature period, the dissolved oxygen concentration can be decreased to 1–2 mg/L, along with reductions of 673.26 t in PAC dosage and 442.33 t in sodium acetate dosage, resulting in a decrease of 1557.24 t CO₂-eq in indirect carbon emissions from chemicals used. Annual operating costs can be reduced by USD 1.49 million. The optimization and simulations of wastewater treatment plants under different temperature conditions can provide empirical evidence for saving energy and reducing emissions in cold-region urban wastewater treatment plants.