Search Results (40)

Yeast surface display is a powerful strategy for enzyme immobilization and whole-cell biocatalysis; however, the intracellular processing of heterologous enzymes during secretion and anchoring remains poorly understood. In this study, a GH5 endoglucanase gene (eglS, 1.4 kb) from Bacillus subtilis, originally isolated from a paper mill effluent, was cloned into the pYD1 vector and expressed in Saccharomyces cerevisiae EBY100 using the Aga1–Aga2 surface display system. The recombinant strain produced clear degradation halos on carboxymethyl cellulose (CMC) plates, confirming cellulolytic activity at the whole-cell level. Zymographic analysis revealed multiple active enzyme forms depending on the cellular fraction analyzed. Intracellular extracts displayed active bands ranging from 70 to 57 kDa, consistent with immature or partially processed Aga2 fusion proteins, whereas cell wall-associated fractions showed active bands between 55 and 35 kDa, suggesting proteolytic processing during secretion and surface anchoring. The apparent specific activity of the cytoplasmic fraction was 5.33 ± 0.31 U mg⁻¹, while the cell wall-associated fraction exhibited a higher apparent specific activity (58.4 ± 10.1 U mg⁻¹). Although these values were obtained from non-purified fractions and therefore do not represent intrinsic enzymatic constants, they indicate a relative enrichment of catalytically active enzyme in the cell wall-associated fraction, consistent with functional surface display. The presence of multiple active enzyme forms and the enhanced catalytic efficiency observed in the cell wall-associated fraction suggest that the engineered yeast strain may serve as a promising whole-cell biocatalyst, with potential applications in consolidated bioprocessing (CBP) strategies for lignocellulosic biomass conversion. Full article

The discharge of untreated paper mill effluent poses significant ecological risks due to its high organic and nutrient loads. This study aimed to assess the phytoremediation potential of Azolla pinnata for treating paper mill effluent. Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling approaches were applied and optimization was used for pollutant removal and plant biomass production. Experiments were designed using a Central Composite Design with two independent variables: effluent concentration (0, 50, and 100%) and plant density (10, 20, and 30 g per container). The responses measured were biochemical oxygen demand (BOD), chemical oxygen demand (COD) removal efficiencies, and final biomass yield after 16 days of exposure. RSM produced statistically significant (p < 0.05) second-order regression models for all three responses (coefficient of determination; R² > 0.98), while ANN showed slightly lower prediction errors within the experimental range studied. Maximum observed removal efficiencies were 91.74% for BOD, 80.91% for COD, and 92.66 g biomass yield under 50% effluent concentration and 30 g plant density. Optimization via both models suggested closely comparable operating conditions (79% effluent concentration and 29 g biomass) for optimal performance. The results indicate that A. pinnata demonstrates potential as a low-cost, nature-based treatment system for industrial effluent remediation under controlled conditions. The integration of data-driven optimization with biological treatment contributes to sustainable effluent management strategies by reducing chemical inputs, minimizing energy demand, and enabling biomass generation with potential downstream valorization. Full article

The Malaysian palm oil industry faces significant environmental challenges from Palm Oil Mill Effluent (POME), a high-strength wastewater. Conventional ponding systems for POME treatment are inefficient, land-intensive, and major sources of methane emissions. Zero Liquid Discharge (ZLD) presents a transformative solution by eliminating effluent discharge while recovering water, energy, and nutrients, aligning with circular economy principles. This review provides a critical techno-economic and Environmental, Social, and Governance (ESG) assessment of ZLD integration for POME management in Malaysia. A key contribution is the novel framework for categorizing ZLD integration at three strategic points in the treatment process, supported by comparative quantitative data on performance and costs. The review thoroughly examines hybrid technologies and delivers an in-depth analysis of the Social and Governance implications of ZLD integration, alongside its Environmental benefits, demonstrating ZLD’s role in meeting regulatory demands and advancing the United Nations Sustainable Development Goals (SDGs). The paper concludes by exploring future directions, including digital twin integration and novel financing, positioning ZLD as a strategic imperative for the industry’s long-term viability and global competitiveness. Full article

Unregulated irrigation with partially industrial effluents may lead to heavy metal contamination in crops and pose significant human health risks, especially in developing countries like India. Therefore, the present study aimed to quantify six heavy metals (Cd, Cr, Cu, Fe, Mn, and Zn) in soil and wheat irrigated with paper mill effluent, assess plant responses, and evaluate associated health risks for consumers. For this, a field study was conducted across ten sites (five effluent-irrigated, five borewell-irrigated as control), analyzing soil and wheat tissues for metal concentrations and calculating risk indices including bioaccumulation factor (Bf), translocation factor (Tf), Dietary Intake of Metals (DIM < 1), Health Risk Index (HRI < 1), and Target Hazard Quotient (THQ < 1). Results indicated high concentrations of Cd and Cr in effluent-irrigated soils and wheat tissues (root > stem > leaves > grains) compared to control sites, with some values exceeding permissible limits. Although the THQ values for heavy metals were below 1, indicating a low immediate health risk, concentrations of Cd and Cr in both soil and crop tissues exceeded acceptable safety standards. This study provides empirical evidence supporting the need for effluent treatment and policy interventions to mitigate agricultural contamination from the use of industrial effluents and protect public health. Full article

This paper presents a strategic planning model aimed at optimizing the economic and environmental impacts of palm oil mill effluent (POME) treatment systems. The model determines the optimal selection of POME treatment systems to minimize the environmental impact, specifically focusing on three systems: an anaerobic digester tank system (ADT), a covered lagoon system (CL) with biogas capture, and an open pond system (OP). The model incorporates constraints related to fresh fruit bunch (FFB) production, POME generation, the biological oxygen demand (BOD), the chemical oxygen demand (COD), and carbon dioxide (CO₂) emissions. The optimization framework, formulated as a mixed-integer linear programming (MILP) model, is solved using the GAMS 40.1.0 software. Integer decision variables are used to represent the choice of POME treatment system that minimizes the environmental impact. The study specifically considers the ADT, CL, and OP systems, with the results indicating that the ADT system is the most effective in reducing the BOD, COD, and CO₂-equivalent emissions, thereby highlighting its environmental benefits. The model selects the ADT treatment system, which exhibits the lowest COD, BOD, and CO₂e emissions. Specifically, the COD registered an 85% reduction, from 84,830 mg/L to 12,725 mg/L. The BOD level was reduced by 88%, resulting in a BOD level of 41,208 mg/L to 4945 mg/L. The minimum CO₂e emissions that could be achieved was about 3173 t CO₂e per annum. This model provides a valuable tool for governmental agencies and policymakers to guide the private sector in developing environmentally sustainable POME treatment strategies. Full article

In this research, the decomposition of toxic organics from pulp and paper mill effluent by the sequential application of ozonation and biodegradation was studied. Ozonation, as a pre-treatment, was executed to transform the initial pollutants into less toxic compounds (such as organic acids of low molecular weights). Biodegradation was executed during three days with acclimated microorganisms that were able to complete the decomposition of the initial organic mixture (raw wastewater) and to achieve a higher degree of mineralization (85–90%). Experiments were performed under three different conditions: (a) only ozonation of the initial contaminants, (b) only biodegradation of residual water without previous treatment by ozone and (c) ozonation followed by biodegradation performed by acclimated microorganisms. In the case of 72 h of biodegradation, the mineralization efficiency reached 85% and 89% after 30 and 60 min of ozonation, respectively. The no significant difference in this parameter coincided with the calculated generalized microorganisms’ consortia specific growing rate ${\mu }_{max}$ that was reduced from 2.08 × 10⁻³ h⁻¹ to 6.05 × 10⁻⁴ h⁻¹ when the ozonation time was longer. The identification of the organics composition by gas chromatography with mass detector (GC-MS) before and after treatments confirmed that the proposed combined process served as a more efficient alternative to secondary and tertiary treatments (mineralization degree between 60 and 80% in average) of the paper industry wastewater. Full article

This study assesses the efficacy of three white-rot fungi—Bjerkandera adusta, Phanerochaete chrysosporium, and Trametes versicolor—in degrading synthetic dyes and lignin in pulp and paper mill effluents, which annually contribute around 40,000 million cubic meters of dyed waste. Exploiting the structural resemblance of dyes to lignin, the fungi utilize ligninolytic enzymes—lignin peroxidase, manganese peroxidase, and laccase—to break down the pollutants. Initial mycoremediation trials in synthetic dye solutions with Direct black 80, Direct yellow 11, Basic brown 1, Orange II, and Red 8 BLP achieved decolorization rates of 70–80% within 7 days, except for Red 8 BLP. Both soluble and insoluble lignin fractions were significantly reduced, with an overall removal rate of 80–90%. Contrary to prior beliefs about the recalcitrance of azo dyes, B. adusta demonstrated substantial biodegradation capabilities, even on non-lignocellulosic substrates, such as dairy waste. The decolorization efficacy varied with dye structure, suggesting that efficiency should not be judged solely on color reduction. Remarkably, B. adusta also effectively decolorized and removed lignin from actual mill effluents without pH alteration, indicating a viable low-cost bioremediation strategy. This invites further investigation into optimizing B. adusta for industrial wastewater biodecolorization, especially in the field of PAHs (Polycyclic Aromatic Hydrocarbons) and EDCs (Endocrine Disrupting Chemicals). Full article

The aim of wastewater treatment plants (WWTPs) is to clean wastewater before it is discharged into the environment. Real-time monitoring and control will become more essential as the regulations for effluent discharges are likely to become stricter in the future. Model-based soft sensors provide a promising solution for estimating important process variables such as chemical oxygen demand (COD) and help in predicting the performance of WWTPs. This paper explores the possibility of using interpretable model structures for monitoring the influent and predicting the effluent of paper mill WWTPs by systematically finding the best model parameters using an exhaustive algorithm. Experimentation was conducted with regression models such as multiple linear regression (MLR) and partial least squares regression (PLSR), as well as LASSO regression with a nonlinear scaling function to account for nonlinearities. Some autoregressive time series models were also built. The results showed decent modelling accuracy when tested with test data acquired from a wastewater treatment process. The most notable test results included the autoregressive model with exogenous inputs for influent COD (correlation 0.89, mean absolute percentage error 8.1%) and a PLSR model for effluent COD prediction (correlation 0.77, mean absolute percentage error 7.6%) with 20 h prediction horizon. The results show that these models are accurate enough for real-time monitoring and prediction in an industrial WWTP. Full article

The effluents generated in the paper industry, such as black liquor, have a high content of lignin and other toxic components; however, they represent a source of lignin-degrading bacteria with biotechnological potential. Therefore, the present study aimed to isolate and identify lignin-degrading bacteria species in paper mill sludge. A primary isolation was carried out from samples of sludge present in environments around a paper company located in the province of Ascope (Peru). Bacteria selection was made by the degradation of Lignin Kraft as the only carbon source in a solid medium. Finally, the laccase activity (Um-L⁻¹) of each selected bacteria was determined by oxidation of 2,2′-azinobis-(3-etilbencenotiazolina-6-sulfonate) (ABTS). Bacterial species with laccase activity were identified by molecular biology techniques. Seven species of bacteria with laccase activity and the ability to degrade lignin were identified. The bacteria Agrobacterium tumefasciens (2), Klebsiella grimontii (1), and Beijeinckia fluminensis (1) were reported for first time. K. grimowntii and B. fluminensis presented the highest laccase activity, with values of 0.319 ± 0.005 UmL⁻¹ and 0.329 ± 0.004 UmL⁻¹, respectively. In conclusion, paper mill sludge may represent a source of lignin-degrading bacteria with laccase activity, and they could have potential biotechnological applications. Full article

Sludge generation as an organic by-product of wastewater treatment has seen a consistent increase worldwide due to population growth and industrial activities. This poses a chronic challenge regarding management options and environmental concerns. The agricultural valorization of unconventional organic materials has become inevitable, especially in semi-arid and arid countries that suffer from depleted soils and shortages in farm manure supply. High-income countries have also been interested in this recycling practice to mitigate landfilling or incineration issues. Sewage and some industrial sludges contain a complex mixture of beneficial and harmful substances, which varies with the origin of effluents. Therefore, sludge land application should be well managed in order to achieve sustainable agro-environmental goals. This review paper focuses on different aspects related to sludge reuse in agriculture, starting by investigating the diversity of sludge types and composition. In addition to the preponderant urban sewage sludge, the less-studied industrial sludges, such as those generated from pulp and paper mills or gas-to-liquid industries, are hereby addressed as well. Then, post-land application effects are discussed in relation to sludge quality, dose, and reuse conditions. The present paper also examines the disparities between guidelines that determine sludge conformity for land application in various countries or regions. Accordingly, special attention is given to increasing risks related to emerging pollutants in sludge such as pharmaceuticals, which have been overused since the outbreak of COVID-19 pandemic. This exhaustive investigation will assist the establishment of sustainable strategies for the safe agricultural reuse of biosolids. Full article

This recent study aims to evaluate the efficacy of membrane filtration on recovery of water resource from agro-waste such as bagasse, crop-based pulp and paper mill waste. A mini pilot scale membrane system having a combination of pre-treatment filter unit (pre-filter, sediment filter and pre-carbon filter), ultra-filtration and reverse osmosis with spiral wound configuration were employed to evaluate the water reuse efficacy of effluent coming from the secondary clarifier of the conventional treatment plant of the mill. The operational conditions were optimized using Taguchi method at pH 8, temperature 32 °C, and pressure 2 bar and a flow rate of 60 l/hr. The qualities of the effluent from the secondary clarifier, and the permeate from both the combination, viz. Combination 1 (pre-treatment + ultra-filtration) and Combination 2 (pre-treatment + ultra-filtration+ reverse osmosis) were analyzed and the percentage reduction in pH, TDS, TSS, BOD, COD, Color, Lignin, Potassium and Sodium were calculated. The elimination of TDS, COD and BOD with Combination 1 was not promising (<22%). However, the installation of a RO membrane greatly reduced (>88%) the contaminants in both paper mill effluents. The obtained qualities of water from all the combinations were compared with the tolerance standard for reuse as process water. The quality of effluent from the secondary clarifier did not agree with any class of water quality. The permeate from the combination of pre-treatment and UF sufficiently reduced the TSS to reach the requirement. However, the combination of (pre-treatment + UF + RO) adequately complied with the quality standard required for reuse in the making of all grades of paper. Full article

Heavy metal pollution from industrial wastewaters has become an issue of global concern. These wastewaters are frequently used for inland irrigation which possess a serious risk of heavy metal contamination of both soil and cultivated crops. The problem is more common in developing countries like India where industrial wastewaters are often discharged without appropriate treatments. Therefore, this study aimed at assessing the impact of paper mill effluent for irrigation on the growth, productivity, and heavy metal accumulation potential of two rice (Oryza sativa L.) varieties (PB-1121 and PR-121). Water, soil, and rice crop samples were obtained from the vicinity of Saharanpur city, Uttar Pradesh, India, and subsequently analyzed for selected physicochemical and heavy metal parameters. Results showed that paper mill effluent and nearby Kali River water had significant (p < 0.05) loads of pollutants that impacted the soil properties. Moreover, the maximum plant height (123.48 ± 4.86 and 98.83 ± 2.02 cm); total chlorophyll (6.70 ± 0.25 and 6.64 ± 0.17 mg/g); leaf carotenoids (0.67 ± 0.08 and 0.63 ± 0.05 mg/g); starch content (71.08 ± 2.05 and 72.60 ± 1.63%); amylose content (25.10 ± 1.32 and 20.28 ± 1.24%); crop yield (4270.20 ± 75.12 and 5830.58 ± 63.10 kg/ha); and straw yield (5472.05 ± 93.90 and 6683.76 ± 61.26 kg/ha) of PB-1121 and PR-121, respectively, were observed using Kali River water irrigation source as compared to paper mill effluent and borewell water. The order of heavy metal accumulation in rice straw and grain followed: Fe > Mn > Zn > Cu > Cr > Cd. Similarly, the bioaccumulation factors for heavy metals in rice straw and grain were observed as >1 and <1, respectively. Moreover, the target hazard quotient (THQ) showed that there was no potential health risk of heavy metal in rice irrigated with contaminated water supply except for the PR-121 variety irrigated with Kali River water where the health risk index (HRI) value exceeded the limit (>1). As a result, the findings of this study provide useful information about the current state of heavy metal pollution and the health risks associated with rice crops irrigated with contaminated water sources. Full article

Agro-waste management processes are evolving through the development of novel experimental approaches to understand the mechanisms in reducing their pollution levels efficiently and economically from industrial effluents. Agro-industrial effluent (AIE) from biorefineries that contain high concentrations of COD and color are discharged into the ecosystem. Thus, the AIE from these biorefineries requires treatment prior to discharge. Therefore, the effectiveness of a continuous flow bioreactor system (CFBS) in the treatment of AIE using hybrid waste sludge biochar (HWSB) was investigated. The use of a bioreactor with hydraulic retention time (HRT) of 1–3 days and AIE concentrations of 10–50% was used in experiments based on a statistical design. AIE concentration and HRT were optimized using response surface methodology (RSM) as the process variables. The performance of CFBS was analyzed in terms of COD and color removal. Findings indicated 76.52% and 66.97% reduction in COD and color, respectively. During biokinetic studies, the modified Stover models were found to be perfectly suited for the observed measurements with R² values 0.9741 attained for COD. Maximum contaminants elimination was attained at 30% AIE and 2-day HRT. Thus, this study proves that the HWSB made from biomass waste can potentially help preserve nonrenewable resources and promote zero-waste attainment and principles of circular economy. Full article

This paper presents an improved artificial neural network (ANN) training using response surface methodology (RSM) optimization for membrane flux prediction. The improved ANN utilizes the design of experiment (DoE) technique to determine the neural network parameters. The technique has the advantage of training performance, with a reduced training time and number of repetitions in achieving good model prediction for the permeate flux of palm oil mill effluent. The conventional training process is performed by the trial-and-error method, which is time consuming. In this work, Levenberg–Marquardt (lm) and gradient descent with momentum (gdm) training functions are used, the feed-forward neural network (FFNN) structure is applied to predict the permeate flux, and airflow and transmembrane pressure are the input variables. The network parameters include the number of neurons, the learning rate, the momentum, the epoch, and the training functions. To realize the effectiveness of the DoE strategy, central composite design is incorporated into neural network methodology to achieve both good model accuracy and improved training performance. The simulation results show an improvement of more than 50% of training performance, with less repetition of the training process for the RSM-based FFNN (FFNN-RSM) compared with the conventional-based FFNN (FFNN-lm and FFNN-gdm). In addition, a good accuracy of the models is achieved, with a smaller generalization error. Full article

The coffee processing industry produces toxic and low biodegradable effluent, which can pollute water bodies. A pre-treatment study on coffee effluent using a dual-media biofilm reactor (DM-BR) containing sand and Hexafilter (HEX) was conducted alongside a control biofilm reactor (C-BR) containing sand media. The novelty of this study lies in the use of dual media in biofilm reactor (DM-BR) for real coffee effluent treatment, where these processes were used individually in previous studies. The performance of DM-BR and C-BR in treating coffee effluent were investigated at different hydraulic retention times (HRTs), 24, 48 and 72 h, and the degrading bacteria were identified. Both biofilm reactors were inoculated with a recycled paper mill-activated sludge and acclimatised for 97 days. The DM-BR displayed the highest removal of chemical oxygen demand (COD) and NH₄⁺-N at 47% and 38%, respectively, within 48 h of HRT, whereas colour and tannin–lignin reached maximum average removal of 21% and 29%, respectively, at 24 h of HRT. The combination of sand and HEX media in a system showed COD and NH₄⁺-N removal improvement at 48 h of HRT and encouraged a variety of bacterial species growth. Bacterial characterisation analysis revealed Proteobacteria to be dominant. Full article