A performance optimization method of multi-phase extraction for soil remediation based on multi-loops coordinated control

Liman Yang; Zixu Fan; Cong Ye; Zhao Zhang; Junjun Wang

doi:10.4491/eer.2024.427

Environ Eng Res > Volume 30(3); 2025 > Article

Yang, Fan, Ye, Zhang, and Wang: A performance optimization method of multi-phase extraction for soil remediation based on multi-loops coordinated control

Research

Environmental Engineering Research 2025; 30(3): 240427.

Published online: October 25, 2024

DOI: https://doi.org/10.4491/eer.2024.427

A performance optimization method of multi-phase extraction for soil remediation based on multi-loops coordinated control

Liman Yang^1,^†, Zixu Fan¹, Cong Ye¹, Zhao Zhang², Junjun Wang²

¹School of Automation Science and Electrical Engineering, Beihang University, Beijing 100190, China

²Center Soil Remediation Research Institute (Shenzhen) Co., Ltd, Shenzhen 518101, China

^†Corresponding author: E-mail: ylm@buaa.edu.cn, Tel: +86-18611437017, ORCID: 0000-0002-0692-1498

Received July 11, 2024 Revised October 14, 2024 Accepted October 24, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Soil pollution has been a global environmental problem. Multi-phase extraction (MPE) is one of the most widely used soil remediation technologies. However, this technology has the problems of low efficiency and high energy consumption, which seriously limit the progress of soil remediation work. In this study, with the aim to improve soil remediation efficiency and save energy, a performance optimization method based on multi-loops coordinated control for MPE was proposed. The performance optimization method combined process parameters optimization and control strategy optimization. In the process parameters optimization, based on the established multiphase flow migration model and MPE system model, the optimal parameters of MPE were obtained. In the latter, the multi-loops coordinated control strategy was used to balance the system’s perturbations and control the concentration of pollutants in the extraction pipeline, the extraction vacuum, the adsorption pressure and inlet flow rate of the activated carbon filter. To verify the effectiveness of the optimization method, it was compared with the actual engineering operation strategy, i.e., single loop separation control. Under the same conditions, the optimized method can improve efficiency by 15.4% and save 12.5% energy. Thus, this study can be used as an advantageous tool for the design of MPE.

Keywords: MPE, Multi-loops coordinated control, Performance optimization, Polluted soil remediation, Process parameters optimization

Graphical Abstract

Keywords: MPE, Multi-loops coordinated control, Performance optimization, Polluted soil remediation, Process parameters optimization

1. Introduction

Soil pollution has become an essential global environmental problem [1–3]. Soil pollution is cumulative, hidden, lagging, inhomogeneous, and poorly reversible, which makes remediation extremely difficult [4–5]. Among the various soil remediation technologies, MPE is one of the most widely used soil remediation technologies, with the advantages of quick results and wide adaptability [6]. It can simultaneously remediate pollutants in groundwater, air inclusion zones and aquifer soil, recover free-phase pollutants and prevent the migration of underground polluted water. In addition, MPE is especially suitable for the remediation of volatile and easy-to-flow Non-Aqueous Phase Liquids (NAPL).

However, MPE has the problems of low efficiency and high energy consumption, and billions of dollars are spent on soil remediation globally each year, which severely limits the popularization and advancement of soil remediation. It shows an urgent need for an optimization method of MPE for reducing energy consumption and improving efficiency. At present, scholars around the world have carried out extensive research on soil remediation technology [7–8].

In the research of multiphase fluids migration, the capillary equations can simulate the migration process of multiphase fluids and their distribution in soil [9]. Among, the migration process of volatile organic pollutants (VOPs) is influenced by various factors, and the mass transfer characteristics of VOPs can only be obtained by fully considering the coupling effect between factors [10–11]. More recently, Lin et al. [12] researched the gas-liquid-solid multiphase fluids mixing mass transfer process by establishing a coupled model of fluid volume and discrete elements and revealed the mass transfer mechanism under the expansion perturbation, which provided a practical reference for related applications.

In the research of MPE, using numerical models to simulate the extraction process of the MPE, shows that the vacuum is the most essential parameter affecting the extraction efficiency [13]. This conclusion was also confirmed by more realistic analysis of engineering data [14]. The regulating valve and the vacuum pump in the MPE are the main equipment affecting the vacuum [15]. Similarly, for the soil vapor extraction (SVE) system, Shi et al. [16] improved SVE extraction efficiency by optimizing extraction methods. There studies of the SVE have reference value for the optimization of the MPE.

It can be shown that most of the above studies focus on principle analysis, which is of some reference to this study. Only a tiny number of scholars studied the effects of system parameters on system efficiency, but they stayed in the experimental analysis stage, and didn’t propose corresponding optimization methods [17]. Even more rarely scholars had proposed ways to improve the efficiency of MPE from the equipment perspective. This study will propose state-of-the-art parameter optimization and control optimization methods for the MPE to improve efficiency and save energy.

The MPE process flow, optimization solutions, soil mass transfer and multiphase fluid migration models are discussed in this paper. It also proposes the MPE extraction model, process parameters optimization, MPE transfer model, and control strategy optimization, and concludes with key findings.

2. Optimization Solutions

2.1. MPE System

The MPE is an in-situ remediation technology that simultaneously extracts gases, liquids, and free-phase pollutants from the polluted area to the surface, then performs multiphase separation and clean treatment. The process flow and main equipment of MPE are shown in Fig. 1.

The technology is composed of three main processes: multiphase extraction, multiphase separation, and pollutants treatment. The main equipment includes vacuum pumps, transfer pipelines, gas-liquid separators, multiphase separators, control systems, treatment equipment, etc.

Extraction: two vacuum pumps operate to generate a vacuum to pump the pollutants into the equipment through the extraction well.
Separation: the extraction mixture is separated by a gas-liquid separator and then transported to the treatment equipment.
Treatment: the aqueous phase in the buffer tank is transported to the liquid-activated carbon filter by a booster pump for adsorption treatment.

Among them, the extraction rate greatly determines the remediation efficiency of the MPE. According to the existing engineering experience, the increase of air flow rate facilitates the removal of pollutants from the soil surface and pore space, thus improves the remediation efficiency of the system. When the gas-phase concentration of pollutants is greater than the equilibrium gas-phase concentration of the adsorption, aqueous, and nonaqueous phases in the soil, increasing the extraction rate facilitates the removal of pollutants and speeds up the remediation process [11]. When the phase equilibrium is reached, and the slow diffusion effect occurs, the increase in extraction rate does not significantly affect the remediation effect. If the extraction rate is too high, it will also increase the load on other equipment.

At present, MPE uses single-loop separation control, which does not provide timely feedback and regulation of many coupled variables, such as vacuum, gas-liquid separator level, and inlet flow and pressure of the activated carbon filter, leading to increased risk of system runaway and pressure explosion. To avoid this problem, the equipment works at a fixed frequency in reality, resulting in low system efficiency and high energy consumption, which seriously limits the popularization and advancement of soil remediation globally. Therefore, the performance optimization of MPE is of great importance [14].

2.2. Optimization Solutions

This study proposes a method to optimize the performance of MPE based on multi-loops coordinated control. By optimizing the process parameters and the system control strategy, the efficiency of MPE is increased and energy consumption is reduced. Fig. 2 shows the technical lines and main contributions of this study.

Analysis of soil mass transfer: based on Darcy’s law and the mass conservation law to establish a multiphase fluids migration model of soil, simulated the diffusion of pollutants in soil and the reflux of extraction under different boundary conditions and initial conditions, analyzed the effect of vacuum on the MPE performance, and laid a foundation for the parameter optimization.
Optimization of process parameters: the extraction model of MPE was established, the effects of the air regulating valve opening and the operating frequency of the vacuum pump on the extraction vacuum and extraction flow rate were researched, and the optimal parameters of MPE were derived based on theoretical analysis and numerical simulation.
Optimization of control strategy: the liquid phase transmission model of MPE is established. The dynamic equations of the pipeline during the frequency adjustment are derived, the flow and pressure of the inlet of the liquid activated carbon filter calculation formula is derived. The cooperative control and cascade control are adopted to control the critical parameters of MPE, and the uniform control is adopted to balance the perturbation of the system’s liquid-phase transmission link.

Based on the above, the efficiency and energy consumption before and after the optimization are compared through MATLAB simulation analysis to verify the effectiveness of the optimization method.

3. Energy Management Strategy

3.1. Multiphase Fluids Migration Model

Polluted soils typically contain air, water and NAPL. Among, NAPL is the main phase of the pollutants, and its migration process in soil is very complicated [18]. Fig. 3 shows phase transformation of NAPL in soil.

In Fig. 3, from a microscopic point of view, the migration process of NAPL in soil is simultaneously affected by organic colloids, inorganic colloids and microorganisms, which are continuously adsorbed, decomposed and transformed [19]. They enter groundwater and underground pores by dissolution and volatilization [20], and then continue to be transported by convection and dispersion. The process of fluids mass change is shown in Fig. 4.

The mass balance equation is given in Eq. (1):

(1)

\frac{\partial (φ S_{α} ρ_{α})}{\partial t} + \nabla (φ S_{α} ρ_{α} q_{α}) = Σ_{β} E_{α, β}

where ∑_βE_αβ is the total amount of compound β transferred to α phase; φ is soil porosity; S_αis the saturation of α phase; ρ_α is the density of α phase; q_α is the flowrate of α phase. Based on Darcy Law, α phase can represent a variety of phases, such as gas phase (g), NAPL phase (o) or aqueous phase (w). The mass balance equations can model the pollutants’ diffusion and extraction processes in soil under different initial conditions and boundary conditions [21–23].

3.2. Model Simulation and Results Analysis

3.2.1. Diffusion simulation

The diffusion process of pollutants in soil is modeled and the initial conditions for the extraction simulation are obtained based on mathematical model for MPE simulation. Table 1 shows the initial conditions for simulation. Fig. 5 describes the simulation environment.

To simulate the diffusion of pollutants, 8,000 L of pollutants were injected into the soil for simulation. The diffusion breadth of the pollutants in the soil reached 8 m in 15 days, and the distribution of the pollutants stabilized after 15 days. After about 2 months, the diffusion of NAPL in the soil reached equilibrium. About 7,000 L of NAPL leached into the soil, and about 1,000 L did not enter the soil due to evaporation and volatilization. Among, about 4,000 L of pollutants became immobile and remained in the soil, and about 3,000 L became mobile. Above diffusion results are basically consistent with the simulation results of Kacem [9]. These results will be used as the initial states for subsequent MPE extraction simulation.

3.2.2. Extraction simulation

Vacuum is the kernel parameter that affects the efficiency of MPE, while other parameters have little effect [13–15]. To research the effect of vacuum on the system efficiency, the extraction simulation was conducted. Based on engineering experience, the vacuum parameters are set to P_well1=15 kPa, P_well2=10 kPa, P_well3=5 kPa respectively, and other parameters remained the same. This simulation was conducted using the single-loop separation control strategy. The results are presented in Table 2 and Fig. S1.

The total amount of pollutants extracted under three vacuum levels were 6791 L, 6483 L and 4738 L, and the removal rates were 95.63%, 92.61% and 67.69%, respectively. From the above results, it is concluded that reducing the vacuum makes pollutants migration difficult, which reduces the remediation efficiency. However, the extraction efficiency did not improve significantly with increasing vacuum. Considering efficiency and cost, more vacuum was not always better, instead there existed an optimal working interval.

However, the regulation of the vacuum requires the cooperation of several equipment in MPE. Among, the regulating valve opening and the operating frequency of the vacuum pump are a pair of coupled variables, both of which affect the vacuum and extraction flow rate. Therefore, it is necessary to establish the model of regulating valve and vacuum pump to analyze the effect of the coupling of the two on the extraction efficiency and optimize its parameters. However, the single loop separation control used in actual engineering cannot accurately control the coupled variables. In this study, parameters optimization and control strategy optimization of MPE will be carried out to improve the remediation efficiency of the system.

4. Original Machine Test and Digital Prototype Correction

4.1. Effects of Regulating Valves on Extraction

4.4.1. Model of regulating valves

Consider the regulating valve as a thin-walled hole and establish a pressure-flow equivalent model for the thin-walled hole. Express the flow characteristics of a regulating valve in terms of the flow characteristics of the thin-walled hole. The equivalent model is shown in Fig. S2.

In Fig. S2, where p₁ and p₂ are the pressure of the fluid before and after the thin-walled hole; θ₁ and θ₂ are their temperatures; ρ₁ and ρ₂ are their densities; S is the area of the thin-walled hole; S_e is its effective flow area; u₂ is its fluid flow rate at the outlet.

According to Bernoulli’s equation and the adiabatic process equation, the relationship between the mass and flow rate and the pressure before and after the thin-walled hole can be obtained in Eq. (2):

(2)

G = {\begin{matrix} S_{e} ρ_{1} \frac{K_{G}}{\sqrt{θ_{1}}} \\ S_{e} ρ_{1} \frac{K_{G}}{\sqrt{θ_{1}}} \cdot 2 \sqrt{\frac{ρ_{2}}{ρ_{1}} (1 - \frac{ρ_{2}}{ρ_{1}})} \end{matrix}

where

K_{G} = \sqrt{\frac{k}{R}} {(\frac{2}{k + 1})}^{\frac{k + 1}{k - 1}}

, k is the specific heat of air; R is the gas constant of air. According to the actual project, the opening circumference of the regulating valve was set to 135 mm and the spool stroke to 46 mm, so the maximum opening area was 6210 mm².

4.1.2. Effects of regulating valve openings on vacuum

The regulating valve controls the extraction vacuum by controlling the fresh air flow. To research the effects of regulating valve opening on the vacuum, simulation was carried out, keeping the other parameters unchanged, only the regulating valve opening was changed, and the opening was set as follows: from 0 to 100% in 20% intervals. The simulation results are shown in Fig. S3(a). The results show that the absolute pressure changes from 59 kPa to 78 kPa as the opening increases. The negative pressure in the extraction well changes from 410 mbar to 220 mbar. It can be found that increasing the regulating valve opening will substantially increase the extraction vacuum and reduce the negative pressure in the well, leading to a reduction in extraction efficiency.

Based on the above conclusions, simulation was again carried out to research the relationship between pressure and flow rate at different openings, The simulation results are shown in Fig. S3(b). It can be found that, under the same degree of opening, the vacuum directly affects the extraction flow rate. Because the vacuum is affected by the regulating valve opening, the regulating valve opening will also affect the extraction flow rate. The next step will be to research the effect of the regulating valve opening on the extraction flow rate.

4.1.3. Effects of regulating valve opening on extraction flow rate

To research the effects of regulating valve opening on extraction flow rate, air flow rate, and total flow rate, simulation was carried out: keeping the other parameters unchanged, only the regulating valve opening was changed, and the opening was set as follows: from 0 to 100% in 20% intervals. First, the difference in extraction flow rate when the regulating valves are fully open and fully closed was analyzed. The results are shown in Fig. S4 (a). The results show that when the regulating valve is fully closed, the extraction flow rate increases rapidly in the initial stage, and after some time, the vacuum tends to be steady, and the pressure distribution in the soil is gradually uniform, leading to a gradual decrease in the flow rate. When the regulating valve was fully opened, the total flow rate increased from 0.9 kg/s to nearly 1.8 kg/s due to the inflow of fresh air, but the extraction flow rate decreased from 0.9 kg/s to 0.5 kg/s. The results confirm that the regulating valve opening significantly decreases the extraction flow rate and the extraction efficiency.

The effect of regulating valve opening on extraction flow rate was quantitatively researched, the fresh air flow rate and extraction flow rate were recorded, and simulation results were obtained. The results are shown in Fig. S4 (b) and Fig. S4(c). The results show that the regulating valve opening affects the flow rate, and the overall trend remains the same. The extraction flow rate went through a peak and then decreased slightly and finally tended to a steady state. The air flow gradually rose and was close to stability. The flow rate of regulating valve was related to the pressure difference between its two ends, and the model was a reflection of the pressure-flow relation without delay, so the change of air flow rate essentially reflected the process of pressure change inside the equipment.

4.2. Effects of Vacuum Pump on Extraction Performance

4.2.1. Vacuum pump model

The vacuum pump is the power source for the MPE and is used to provide negative pressure in the well. The pressure-flow characteristic equation of a vacuum pump can be described by Eq. (3):

(3)

p = a - b Q - c Q^{2}

where Q is the outlet fluid flow of the vacuum pump; p is the outlet pressure of the vacuum pump; a, b, and care the parameters related to the characteristics of the vacuum pump itself.

When the vacuum pump runs at rated speed n₁, its outlet flow rate and pressure are Q₁ and P₁, respectively. The vacuum pump outlet flow, pressure, and speed satisfy the Eq. (4):

(4)

\frac{p_{1}}{p} = {(\frac{Q_{1}}{Q})}^{2} = {(\frac{n_{1}}{n})}^{2}

Bringing Eq. (3) into Eq. (4) gets Eq. (5) and Eq. (6), which is the characteristic curve equation at different frequencies:

(5)

{(\frac{n}{n_{1}})}^{2} p_{1} = a - b Q \frac{n}{n_{1}} - c Q^{2} {(\frac{n}{n_{1}})}^{2}

(6)

p = 0.1915 f^{2} - 0.00563 f Q - 0.000174 Q^{2}

where f is vacuum pump operating frequency. According to Eq. (6), the pressure-flow characteristic curve from 30 Hz to 60 Hz can be obtained, as shown in Fig. S5.

4.2.2. Effects of vacuum pump frequency on vacuum

To eliminate the interference of air flow, the regulating valve was closed in the simulation, and the vacuum pump frequency was set to 20 Hz, 30 Hz, 40 Hz and 50 Hz, respectively. The pressure change in the extraction process was recorded. The simulation results are shown in Fig. S6. The pressure in the extraction well did not directly reach the stable negative pressure but a slow decline process, gradually approaching the stable negative pressure. This is because the gas capacity cavity made the pressure in the well can not be changed abruptly. On the other hand, the pressure in the well decreased significantly as the vacuum pump frequency decreased. The higher the vacuum pump frequency, the higher the extraction capacity, and the faster the pressure in the well decreases, but the longer it taken to reach a steady state. When the vacuum pump frequency was 50 Hz, the pressure in the well stabilizes was about 57.6 kPa (424 mbar), and when the vacuum pump frequency was lowered to 20 Hz, the pressure in the well was only about 95.4 kPa (46 mbar), which was very close to the atmospheric pressure, and there was almost no extraction capacity.

4.2.3. Effects of vacuum pump frequency on extraction flow rate

In the simulation, the regulating valve opening was set to 0 to make the extraction flow have only one input source from the extraction well. The operating frequency of the vacuum pump is too low to damage the motor easily. The actual production follows the requirements set at 30 Hz, the operating frequency range of the simulation was set to 20 Hz to 50 Hz in 10 Hz intervals. The results are shown in Fig. S7. The extraction flow curve first passes through a peak and then slowly decreases to a stable value. The lower the frequency, the shorter the time to reach the peak, and the faster the flow rate decreases. This is because vacuum pump is more sensitive to changes in flow rate at low speeds, and the output pressure of the vacuum pump is more easily affected by the flow rate.

4.3. Optimization of Process Parameters

Process parameters are optimized to keep the MPE in the best working condition, improve efficiency, and save energy. According to the simulation results, only for the removal of pollutants in the soil, the larger the extraction vacuum the better the pollutants removal effect. However, because of the limitations of the capacity of the extraction pipeline, energy consumption, and economic costs, an enormous extraction vacuum is not better, so it is necessary to explore the values of the system parameters at the optimal extraction efficiency.

The fitted equation for the effect of regulating valve opening on vacuum is shown in Eq. (7):

(7)

p_{well} = - 193 r_{v a l u e} + 409.5

where p_well is the extraction vacuum; r_valve is the regulating valve opening. Fig. S8 (a) shows the fitted curve.

The fitted equation for the effect of regulating valve opening on flow rate is shown in Eq. (8):

(8)

{\begin{matrix} F_{w e l l} = - 0.3925 r_{v a l u e} + 0.9084 \\ F_{a i r} = - {0.2812}_{v a l u e}^{2} + 1.526 r_{v a l u e} - 0.002877 \\ F_{t o t a l} = - 0.3111 r_{v a l u e}^{2} + 1.163 r_{v a l u e} + 0.9015 \end{matrix}

where F_well is the extraction flow rate; F_airis the air flow rate; and F_total is the total flow rate. Fig. S8 (b) shows the fitted curve.

The fitted equation for the effect of vacuum pump frequency on vacuum is shown in Eq. (9):

(9)

p_{well} = 0.1396 f^{2} + 1.317 f - 1.54

where f is the vacuum pump frequency. Fig. S8 (c) shows the fitted curve.

The fitted equation for the effect of vacuum pump frequency on flow rate is shown in Eq. (10):

(10)

F = 0.000275 f^{2} + 0.00462 f - 0.004863

where F is the extraction flow rate. Fig. S8 (d) shows the fitted curve.

Eq. (11) describes the constraints affecting the extraction efficiency:

(11)

s . t . {\begin{matrix} 0 < r_{v a l u e} < r_{m a x} \\ 0 < f < f_{m a x} \end{matrix}

An equation with extraction efficiency as the objective function was established as shown in Eq. (12)

(12)

m a x F_{t o t a l} (r_{v a l v e}, f)

The results of Fig. S9 confirm that the regulating valve opening significantly decreases the extraction flow rate and the extraction efficiency. However, too small regulating valve opening will lead to overloading of the vacuum pump, and it is easy to damage the motor when working for a long time, so the opening of the regulating valve is set to 20%, and at this time, the mass flow rate under different frequencies can be obtained by solving the optimization model, and the results are shown in Fig. S9. It can be obtained that the extraction rate is optimal at an extraction vacuum of 10 kPa. When the extraction vacuum was within 10 kPa, the pollutants removal efficiency became significantly better with the vacuum increase. When the vacuum was outside 10 kPa, the removal efficiency of pollutants had not significantly increased. Therefore, 10 kPa was the optimal vacuum for this initial condition, and this vacuum value was used as the initial condition for optimizing the control method. This parameter optimization model was universal, and changing the initial conditions can obtain the corresponding optimal vacuum level.

5. MPE Control Strategy Optimization Method

5.1. Mathematical Model

Fig. S10 describes the liquid phase transfer equipment. Screw pumps and booster pumps are the leading equipment in the liquid transfer chain and are used for level, flow, and pressure control. Eq. (13) describes the change in flow rate, and Eq. (14) is used to calculate the level height:

(13)

Q_{i n} - Q_{o u t} = \frac{d v}{d t} = S_{α} \frac{d h}{d t}

(14)

h = \frac{1}{s_{α}} \int Q_{i n} - Q_{o u t} d t

where Q_in and Q_out are inflow and outflow flows, respectively; h is the height of the liquid level in the separator; V is the volume of liquid; S_£ is the horizontal cross-sectional area in the separator.

The kinematic equation of the pipeline was obtained by integrating, as shown in Eq. (15):

(15)

ρ L \frac{d u}{d t} = \frac{ρ u_{1}^{2}}{2} - \frac{ρ u_{4}^{2}}{2} + p_{1} - p_{4} + ρ g z_{1} - ρ g z_{4} + Δ p - Δ p_{L}

where L is the length of the pipeline; p is the fluid density; u, u₁ and u₄ are the fluid flow rates in the pipeline, buffer tank and activated carbon filter, respectively; z₁ and z₄ are the vertical distance from the center of the buffer tank and activated carbon filter to the datum plane; Δp is the outlet pressure of the pump; Δp_L is the pressure loss of the pipeline.

According to the pump’s characteristic curve, the outlet pressure of pump and flow rate can be described by Eq. (16):

(16)

Q = \frac{- B n - \sqrt{{(B n)}^{2} - 4 A C n^{2} + 4 A Δ p}}{2 C}

where Q is the outlet flow rate of the pump; n is the actual speed of the pump; n₀ is the rated speed of the pump; A=a/n₀², B=b/n₀, C=c; a, b and care fitting parameters of the pressure-flow curve.

Losses in the pipeline are calculated by Eq. (17):

(17)

Δ p_{L} = (λ \frac{L + Σ L_{e}}{D} + Σ ξ) \cdot \frac{ρ g}{2} {(\frac{1}{π D^{2}})}^{2} Q^{2}

where λ is friction coefficient of pipeline; D is the pipeline diameter; ∑ζ is the drag coefficient; ∑L_e is the equivalent length between the buffer tank and the activated carbon filter.

Eq. (15)–(17) form a dynamic mathematical model of the pipeline. The transfer function between flow rate and pump speed, and between flow rate and inlet pressure of the activated carbon filter were obtained by linearizing and calculating the Laplace transformation at the steady state point, as shown in Eq. (18):

(18)

{\begin{matrix} \frac{Q (s)}{n (s)} = \frac{B D + 4 A C n_{0} - B^{2} n_{0}}{2 C (2 b_{L} Q_{0} - D)} \cdot \frac{1}{T s + 1} \\ \frac{p_{4} (s)}{Q (s)} = \frac{1}{2 b_{L} Q_{0} - D} \cdot \frac{1}{T s + 1} \\ T = \frac{ρ L}{A (2 b_{L} Q_{0} - D)} \end{matrix}

where Q₀ is the rated capacity of the pump; T is the coefficient of the transfer function.

Actual project data is shown in Table S1. The transfer function is obtained by bringing the actual engineering data into the equation, as shown in Eq. (19):

(19)

{\begin{array}{l} \frac{Q (s)}{n (s)} = \frac{0.0075}{20 s + 1} \\ \frac{p_{4} (s)}{Q (s)} = \frac{168.3}{20 s + 1} \end{array}

5.3. Control Strategy Optimization

The MPE system controllers include vacuum pumps and regulating valves control for extraction, screw pumps control for separation and booster pumps control for treatment. Currently MPE controllers are single-loop separation control, its independent control loop cannot stabilize the control system parameters or regulate changes in the coupled variables in time, resulting in low system efficiency and high energy consumption. This study innovatively proposes the multi-loops coordinated control, which contains coupled control, uniform control and cascade control. Fig. S11 shows a schematic diagram of the multi-loops coordinated control, where the black box shows the traditional single-loop separated control and the red box shows the added control link.

In the extraction, vacuum and the pollutants concentration are the controlled variables, but these two variables are deeply coupled. When the pollutants concentration exceeds the set point, the regulating valve will open to introduce fresh air to reduce the pollutants concentration, but it will lead to a lower vacuum, it is necessary to increase the frequency of the vacuum pump to get a higher vacuum. So, there is a need to cooperative control the pressure and concentration of the system.

In the separation, liquid level stabilization is achieved by controlling the variable speed pump, and the level of the gas-liquid separator controls the frequency of the screw pump. In addition, the inlet flow rate of the oil-water separator can not fluctuate greatly, as it will affect the oil-water separation efficiency. Therefore, a uniform control strategy was used to control the screw pump to keep the level of the gas-liquid separator stable and the inlet flow rate of the oil-water separator stable.

In the treatment, the booster pump is used as the actuator of the control system to control the inlet flow and pressure of the activated carbon filter, with the pressure control as the primary circuit and the flow control as the sub-circuit. On the basis of stabilizing the buffer tank level, cascade control is used to regulate the inlet flow and pressure of the activated carbon filter, which can reduce the control task of the booster pump.

5.3. Simulation Analysis of Control Strategy Optimization

The MPE system control simulation model was built in MATLAB/Simulink according to the above control strategy. The effectiveness of the performance optimization strategy proposed in this study was verified by analyzing the efficiency and energy consumption of the system before and after optimization. The simulation model is shown in Fig. S12.

5.3.1. Control of vacuum and pollutants concentration

In the simulation, the extraction pump used is a three-phase AC asynchronous motor with an operating frequency range of 30–60 Hz. According to the nameplate of the motor, its rated operating frequency is 50 Hz, rated operating speed is 2900 r/min, the number of pole pairs is 1 pair, and the slew rate is 0.03. Eq. (20) describes the operating speeds at different operating frequencies.

(20)

n = \frac{60 f (1 - s)}{m}

where n is the motor speed, f is the motor operating frequency, mis the number of pole pairs, s is the slew rate.

The vacuum needed to be stabilized at 10 kPa, and the pollutants concentration in the pipeline was set to 20% based on the lower explosive limit. The two control variables needed to be precisely controlled, so the PI controller was used to stabilize the steady-state value at the set value. After parameters adjustment, the PI parameters for pressure control were: K_P=5, T_I=0.023, and the PI parameters for concentration control were: K_P=15, T_I=4. The control results are shown in Fig. S13. The extraction vacuum and the pollutants concentration were stabilized at the set value. There was only 1 min from the startup to the system’s stable operation, and the overshoot of the adjustment process was minimal. When the system runs steadily, the regulating valve opening was about 5%, and the vacuum pump maintained a speed of 2892 rpm.

5.3.2. Control of gas-liquid separator level

The level of the gas-liquid separator was controlled by a screw pump, which required small fluctuation in the outlet flow rate during the adjustment process, and therefore uniform control was used. Uniform control was realized by a P controller. The gas-liquid separator cavity height was set to 1 m, the safety height was set to 0.5 m, and the controller parameter was set to K_P=0.005. When the system ran to 400 s, a perturbation was added to the outlet flow of the pump (the outlet flow was reduced by 15%) to simulate the flow fluctuation in the actual working. Fig. S14 shows the level of the separator and the outlet flow rate of pump.

In Fig. S14, after adding the perturbation, the flow rate and liquid level changes were very smooth, and stabilization was reached again after about 100 s. The control results had the expected effect.

5.3.3. Control of activated carbon filter inlet flow and pressure

The activated carbon filter inlet flow and pressure were controlled by cascade control. The pressure control is the main loop, the PI controller was used to accurately control the inlet pressure of the activated carbon filter, the parameters were set to K_P=10, T_I=0.03. The flow control is a sub-loop, the P controller was used to minimize fluctuations in the inlet flow to the activated carbon filter, the parameter was set to K_P=0.05. The control results are shown in Fig. S15.

In Fig. S15, the flow rate and the pressure precisely stabilized at the set value after about 200 s. At about 400 s, the buffer tank level reached 0.5 m, and the booster pump started working. At about 800 s, the flow rate of the buffer tank reached equilibrium, the outflow was equal to the inflow, and the liquid level was stabilized at 0.52 m. It can be seen that the unified control of pressure, flow rate, and liquid level were realized.

5.4. Comparative Simulation and Results Analysis

To verify the effectiveness of the optimization strategy proposed in this study, a comparative simulation was carried out. Case I is the multi-loops coordinated control, i.e., optimized process parameters and control strategy in this study. Case II is the single-loop separation control, which is the control strategy actually used in the current project. The simulation initial conditions of the soil used the parameters of pollutant diffusion in 0. Used two cases to work for 100 days, the extraction flow rate, pollutant concentration, and other indicators were compared. Fig. S16 illustrates the results.

In Fig. S16, the total extraction flows for Case I and Case II were 1.5×10⁵ m³ and 1.3×10⁵ m³, respectively, and the efficiency was improved by 15.4% for Case I than for Case II. It took 280 and 320 days to process all pollutants for Case I and Case II, consumed 8.736×10⁴ kWh and 9.984×10⁴ kW h of electricity, respectively, and the energy was saved by 12.5% for Case I than for Case II.

6. Conclusion

In this study, a performance optimization method for MPE based on multi-loops coordinated control was proposed. The method combined process parameters optimization and control strategy optimization. On the basis of theoretical derivation and numerical simulation, the optimal process parameters of the MPE were derived. Then, the control strategy was designed according to the system characteristics. Based on the study results, the following conclusions could be made:

With the given engineering parameters, the optimal vacuum in the extraction well was 10 kPa. It can not only improve the extraction efficiency, but also save energy.
The critical parameters of the system were controlled by using cooperative control and cascade control strategies, and the perturbation of the liquid-phase transport link of the system was controlled by using uniform control, which achieved the expected control effect.
The performance optimization strategy in this study was compared with the actual engineering operation strategy. The results show that the optimized method improved the efficiency by 15.4% and saved 12.5% energy for multi-loops coordinated control than for single-loop separation control.

Thus, this study can be used as an advantageous tool for the design of MPE. However, it should be noted that the model developed has some limitations. To use the technology at industrial scale, some experimental results from the field scale are needed for the next step of research.

Supplementary Information

eer-2024-427-supplementary.pdf

Acknowledgments

The Center Soil Remediation Research Institute (Shenzhen) Co., Ltd is warmly acknowledged for the financial support, and the R&D department for facilitating the study. The authors would like to thank the editor and reviewers for their constructive suggestions on this manuscript.

Notes

Conflict-of-Interest Statement

The authors declare no competing financial interests.

Authors Contributions

L. Y. (Associate Professor) directed and instructed the research and the writing of the manuscript. Z. F. (Master student) was in charge of the whole trial and simulation of the project, wrote the manuscript. C. Y. (Master student), Z. Z. (PhD student), and J. W. (PhD student) assisted in editing of the manuscript and the data processing. All authors approved the final manuscript.

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Fig. 1

Process flow and main equipment of MPE.

Fig. 2

Technical lines and main contributions of this study.

Fig. 3

Phase transformation of NAPL in soil.

Fig. 4

Schematic diagram of fluid mass change in each phase.

Fig. 5

Simulation environment.

Table 1

Initial conditions for simulation.

Phase	Name	Sign	Value
Soils	Intrinsic permeability	K̄	3.7×10⁻¹¹ m²
	Porosity	φ	0.4
	Apparent density	ρ_b	1.59 gcm⁻³

Aqueous phase	Density	ρ_w	1000 gL⁻¹
Aqueous phase	Viscosity	μ_w	10⁻³ Pas

Gas phase	Density	ρ_g	1.3 gL⁻¹
Gas phase	Dynamic Viscosity	μ_g	1.8×10⁻⁵ Pas

NAPL (toluene)	Density	ρ_o	867 gL⁻¹
NAPL (toluene)	Dynamic Viscosity	μ_o	5.9×10⁻² Pas

Pollutants	Volume	ν	8000 L

Table 2

Amounts of pollutants extracted at three vacuum levels.

Vacuum	Liquid phase volume/L	Gas phase volume/L	NAPL volume/L	Removal rates
P_well1	2175.2	1799.8	2816	95.63%
P_well2	2000.2	1799.8	2683	92.61%
P_well3	1136.4	1572.6	2029	67.69%