Grey Water Footprint Reduction by Agro-Industrial Biochar for Brewery Wastewater Treatment: A Data-Driven Parametric Model ^†

Abstract

This paper reported the grey water footprint (GWF) mitigation resulting from a brewery industry wastewater treatment using malt dust-derived biochar. The GWF was assessed based on chemical oxygen demand (COD) and total suspended solids (TSS) removal. A new data-driven parametric index (GWFI_BP) was reported that uses the GWF tool. A data-driven model was designed in order to define the impact of the dual advantages of biochar application relative to the Conventional Activated Sludge (CAS) process. A GWF reduction of approximately 21.59% was found for the biochar application.

1. Introduction

Clean water and sanitation is one of the sustainable development goals (SDG-6) that should be achieved by 2030 [1]. Proper and effective industrial wastewater treatment is complementary by integrating circular economy targets in terms of waste valorization [2]. Biochar is an efficient, supportive material in industrial wastewater treatment [3,4] in the last decades. Biochar has earned great significance due to its use in the disposal of a wide array of organic and inorganic pollutants from industrial wastewater [5,6]. From this perspective, this paper researched the grey water footprint (GWF) mitigation originating from brewery wastewater treatment using malt dust-derived biochar. The major aim of this study was to reduce the GWF originating from a brewery wastewater treatment using agro-industrial-based biochar. The scope of this paper was that biochar was able to dispose of the organic materials in wastewater with a larger adsorption capacity in terms of COD and TSS removal. This study was original in that the organic material removal by the agro-industrial biochar application was calculated based on a data-driven parametric model simulated using a Monte Carlo design. The limitations of this work were that the other components of GWF related to wastewater treatment were ignored. Only COD and TSS removal were considered. Also, the other limitation of this work could be that the experimental assays were based on pilot-scale practices.

2. Materials and Method

Experimental design was based on COD and TSS monitoring results of biochar process and wastewater analyses. Wastewater analyses were based on standard methods [7]. The influent characteristics of brewery industry are given in

Table 1. Influent quality.

Parameter	Range
TSS (mg/L)	3200–3567
COD (mg/L)	5814–6000
pH	6.17–6.24
TKN (mg/L)	50–67
OLR (g COD/L d)	5.9–6.15

. It is highly organic wastewater. Biochar was fabricated from malt dust, which was an agro-industrial by-product of a brewery industry in Türkiye by slow pyrolysis under temperatures of 250 (B1), 300 (B2) and 500 °C (B3). The aim of selecting three different temperatures was to achieve optimum treatment efficiency.

An adsorption column was operated, and the mix of 5 g of each biochar (B1 + B2 + B3) was used as the adsorbent. This amount was determined according to the experimental assays to achieve maximum pollutant removal. The volume of treated wastewater was 0.75 L, and the duration of experiments was 1 day. According to the experimental assays, the maximum TSS (up to 74.49%) and COD (up to 96.62%) removal efficiencies were achieved by the mix of biochar. The general adsorption isotherm (Equation (1)) [8] was used for organic material removal during adsorption tests. Seasonal wastewater sampling and measurement were applied:

$$\mathrm{q}\mathrm{e}={\displaystyle \frac{\left(\mathrm{C}\mathrm{O}\mathrm{D}\mathrm{i}-\mathrm{C}\mathrm{O}\mathrm{D}\mathrm{e}\right)\mathrm{W}}{\mathrm{B}}}$$

(1)

where CODi is initial COD concentration (mM) (before adsorption process), CODe is final COD concentration(mM) (after biochar adsorption), W is wastewater volume (L), B is biochar dose (g), and qe is the quantity of organic materials adsorbed onto the biochar (mmol/g).

GWF (m³/d) was calculated using Equation (2) adapted from the methodology by Moreira et al. (2016) [9] for both COD and TSS removal. GWF was determined according to both COD and TSS removal. In Equation (2), Q is the wastewater flow (m³/d), and C_e is the effluent value (mg/L). C_max is the maximum allowable limit of a contaminant, and C_n is the natural value of a contaminant in the discharging point. In this paper, the contaminant parameters were COD and TSS, and two parameters were considered in calculation. C_n is accepted as 0 at discharging point, and the values of C_max were 4000 and 500 mg/L for COD and TSS, respectively, in Türkiye [10].

$$\mathrm{G}\mathrm{W}\mathrm{F}={\displaystyle \frac{\mathrm{Q}({\mathrm{C}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{C}}_{\mathrm{n}})}{{\mathrm{C}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{C}}_{\mathrm{e}}}}$$

(2)

Monte Carlo simulation (@RISK software (trial version 8) Palisade USA) was applied in the data-driven analysis of COD and TSS removal efficiencies by agro-industrial biochar in brewery wastewater treatment. A data-driven parametric index (GWFI_BP) was defined using the main GWF tool, which measured the performance of biochar application. The developed model is shown in Equation (3). This index was in the range of 0–1. If this value converges to 1, the pollution disposal ability is greater.

$${\mathrm{GWFI}}_{\mathrm{BP}}(\mathrm{n})=(({\displaystyle \frac{\mathrm{G}\mathrm{W}\mathrm{F} \mathrm{O}\mathrm{L}\mathrm{R} \mathrm{t} \mathrm{A}}{\mathrm{B} \mathrm{Q}}})/100)\times (\mathrm{C}\mathrm{C})\mathrm{n}$$

(3)

The variables in Equation (3) are defined as below:

GWF: Grey water footprint based on COD/TSS removal (m³/d);

OLR: Organic Loading Rate (g COD/L d);

t: Contact Time (d);

B: Biochar Amount (g);

A: Adsorption Column Volume (L);

Q: Wastewater flow (m³/d);

n: Confidence test number;

CC: Confidence co-efficient (%).

A data-driven model based on the parameters of COD and TSS was designed to define the impact of dual advantages of biochar application relative to the CAS process. This model calculates the removal efficiencies and acceptable discharge limits. Five simulations (n) and 5000 iterations were performed using @RISK software by Palisade (trial version, Ithaca, NY, USA). According to the simulation model (Equation (4)), the confidence co-efficient (CC) was determined. The input data were the GWF (desired output) and the wastewater quality parameters, which were Q, TSS and COD (inputs), as in Equation (4). For each input, the simulation was repeated to determine the co-efficient. Also, an uncertainty analysis was performed using Monte Carlo simulation to determine the confidence interval (Equation (5)). The degree of significance (DS) (R²) was determined. These formulas were automatically derived by Monte Carlo simulation by considering the data set and inputs. The desirable output was the minimum grey water footprint from brewery industry wastewater treatment.

$$\mathrm{CC}=\mathrm{R}\mathrm{i}\mathrm{s}\mathrm{k}\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t}\left(\mathrm{L}\mathrm{o}\mathrm{g}\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\right)+\mathrm{R}\mathrm{i}\mathrm{s}\mathrm{k}\mathrm{L}\mathrm{o}\mathrm{g}\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}(\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{G}\mathrm{W}\mathrm{F};\mathrm{Q},\mathrm{T}\mathrm{S}\mathrm{S},\mathrm{C}\mathrm{O}\mathrm{D})$$

(4)

$$\mathrm{DS} \left({\mathrm{R}}^2\right)=\mathrm{R}\mathrm{i}\mathrm{s}\mathrm{k}\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t}\left(\mathrm{L}\mathrm{o}\mathrm{g}\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\right)+\mathrm{R}\mathrm{i}\mathrm{s}\mathrm{k}\mathrm{L}\mathrm{o}\mathrm{g}\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}(\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{G}\mathrm{W}\mathrm{F},\mathrm{G}\mathrm{W}\mathrm{F}\mathrm{I}\mathrm{B}\mathrm{P}(\mathrm{n}\left)\right)$$

(5)

3. Results and Discussion

According to this study, based on the GWF analysis, malt dust-derived biochar is a good adsorbent of organic substances in industrial wastewater treatment. According to the experimental adsorption assays, B1 had the highest organic material adsorption capacity in terms of COD adsorption (25.79 mmol/g). B2 had a COD adsorption capacity of 25 mmol/g, and B3 had a COD adsorption capacity of 24.91 mmol/g. The average GWF reduction was reported as 21.59% compared to the CAS process. The average value of GWF was 9.86 m³/d for the biochar adsorption process, while this value was 12.57 m³/d for the CAS process. The biochar application was also more efficient than the CAS process in terms of COD and TSS removal. The organic material (COD) removal capacity was improved by 22.15% by the biochar application. The average organic material (COD) removal capacity of the CAS process was 73.4–79.1% for the full-scale brewery industry wastewater treatment plant (

Table 2. Overall results, data set and data interpretation for the mix of B1, B2 and B3 (B1 + B2 + B3) application.

Parameter	Sampling Period-I (Winter)	Sampling Period-II (Autumn)	Sampling Period-III (Spring)	Sampling Period-IV (Summer)	Average Reduction (in Comparison with CAS Process)	Improvement (%)
GWF (m3/d)	7.9	8.45	10.1	12.99	21.59%	* NA
GWFIBP (%)	0.15	0.177	0.2525	0.429	* NA	* NA
CCn (%)	0.19	0.21	0.25	0.33	* NA	* NA
COD removal (%)	87.8	85.5	85.25	82.8	* NA	22.15
TSS removal (%)	75.5	70.35	69.25	68.7	* NA	15.78

* (NA: not applicable).

). Also, TSS removal was enhanced by 15.78% through agro-industrial biochar adsorption in terms of industrial wastewater treatment. This removal rate was 61.8–64.34% for the CAS process in the brewery wastewater treatment. The COD and TSS removal of the biochar application were in the range of 89.7–96.62% and 71.55–74.49%, respectively.

Also, the developed indicator (GWFI_BP) was in the range of 0.15–0.429 (Table 2). The data-driven analysis was performed using Monte Carlo simulation to develop this indicator in a confident manner. The results of this analysis are given in

Table 3. Results of data-driven analysis by Monte Carlo simulation.

Paradigm	Desired Outputs	Inputs	CCn	R2 (%)
5 simulations, 1000 iterations	min GWF	Q	0.10	83.8
		COD	0.12	84.15
		TSS	0.14	85
5 simulations, 1000 iterations	min GWF	TSS	0.165	85.8
		COD	0.179	86.25
		Q	0.27	87
5 simulations, 1000 iterations	min GWF	COD	0.299	87.45
		TSS	0.30	88
5 simulations, 1000 iterations	min GWF	TSS	0.315	88.25
		COD	0.33	89.4

. According to this analysis, the confidence co-efficient was reported to be in the range of 0.10–0.33. The determination co-efficient (R²) was found to be in the range of 83.8 and 89.4%. The degree of significance (R²) is an important indicator of whether this index is trustworthy or not. The analysis results confirmed that the index of GWFI_BP was meaningful in the range of 83.8–89.4%.

In the literature, there are many studies related to brewery industry wastewater treatment. The studies on the removal of organic materials were mainly based on biological treatment techniques [11,12,13]. Goshe et al. (2025), de Jesus Cerqueira et al. (2025), and Sillero et al. (2024) used biochemical methods to treat brewery industry wastewater [11,12,13]. The operations of biological systems (anaerobic and advanced treatment units) are more complex relative to those of biochar application. The other major benefits of biochar application are regeneration and reuse capacity [14,15,16].

4. Conclusions

This paper claimed that malt dust-derived biochar can not only remove the organic substances from wastewater but also achieve the minimization of GWF in terms of water scarcity. This paper demonstrated an average 21.59% reduction in GWF by agro-industrial biochar. The maximum GWF reduction was related to COD removal (12.99 m³/d). The regeneration capacity of malt dust-derived biochar should be further determined to support circular economy ssprinciples for other environmental remedies.