Comparative evaluation of low-cost ceramic membrane and polymeric micro membrane in algal membrane photobioreactor for wastewater treatment

Process innovations and circular strategies for closing the water loop in a process industry

By implementing advanced wastewater treatment technologies coupled with digital tools, high-quality water is produced to be reused within the industry, enhancing process efficiency and closing loops. This paper investigates the impact of three innovation tools (process, circular and digital) in a Solvay chemical plant. Four technologies of the wastewater treatment plant "WAPEREUSE" were deployed, predicting their performance by process modelling and simulation in the PSM Tool. The environmental impact was assessed using Life Cycle Assessment and compared to the impact of the current industrial effluent discharge. The circularity level was assessed through three alternative closed-loop scenarios: (1) conventional treatment and discharge to sea (baseline), (2) conventional and advanced treatment by WAPEREUSE and discharge to sea, (3) conventional and advanced treatment by WAPEREUSE and industrial water reuse through cross-sectorial symbiotic network, where effluents are exchanged among the process industry, municipality and a water utility. Scenario 1 has the lowest pollutants' removal efficiency with environmental footprint of 0.93 mPt/m3. WAPEREUSE technologies decreased COD by 98.3%, TOC by 91.4% and nitrates by 94.5%. Scenario 2 had environmental footprint of 1.12 mPt/m3. The cross-sectorial symbiotic network on the industrial value chain resulted in higher industrial circularity and sustainability level, avoiding effluents discharge. Scenario 3 is selected as the best option with 0.72 mPt per m3, reducing the environmental footprint by 21% and 36% compared to Scenarios 1 and 2, respectively.

Microalgae cultivation trials in a membrane bioreactor operated in heterotrophic, mixotrophic, and phototrophic modes using ammonium-rich wastewater: The study of fouling

In this work, microalgae cultivation trials were carried out in a membrane bioreactor to investigate fouling when the cultures of Chlorellavulgaris were grown under mixotrophic, heterotrophic, and phototrophic cultivation regimes. The Chlorella cultures were cultivated in wastewater as a source of nutrients that contained a high concentration of ammonium. In mixotrophic cultivation trials, the results showed that the elevated contents of carbohydrates in the soluble microbial product and proteins in extracellular polymeric substances probably initiated membrane fouling. In this case, the highest protein content was also found in extracellular polymeric substances due to the high nitrogen removal rate. Consequently, transmembrane pressure significantly increased compared to the phototrophic and heterotrophic regimes. The data indicated that cake resistance was the main cause of fouling in all cultivations. Higher protein content in the cake layer made the membrane surface more hydrophobic, while carbohydrates had the opposite effect. Compared to a mixotrophic culture, a phototrophic culture had a larger cell size and higher hydrophobicity, leading to less membrane fouling. Based on our previous data, the highest ammonia removal rate was reached in the mixotrophic cultures; nevertheless, membrane fouling appeared to be the fundamental problem.

Exploring effects of carbon, nitrogen, and phosphorus on greywater treatment by polyculture microalgae using response surface methodology and machine learning

The microalgae-based wastewater treatment is a promising technique that contribute to achieving sustainable development goals (SDGs), such as SDG-6, "Clean Water and Sanitation". However, it is strongly influenced by the initial composition of wastewater. In this study, the impact of initial organics and nutrient concentration on the removal of total organic carbon (TOC), total carbon (TC), ammonium (NH4+), total nitrogen (TN), and phosphate (PO43-) from greywater using native polyculture microalgae was explored. Response surface methodology was employed along with two machine learning approaches, AdaBoost and XGBoost, to evaluate the interactions among three main factors: TOC, NH4+, and PO43-, and their effects on treatment efficiency. The C/N ratios for achieving maximum TOC and TC removal efficiency of 99.2% and 97.7% were determined to be 10.3, and 65.4-73.6, respectively. Notably, the N/P ratio did not significantly affect their removal. The highest NH4+ removal efficiency, reaching 96.2%, was attained at C/N ratios of 4.3, 24.0, 38.2, and 212.9, coupled with N/P ratios of 0.3, 2.6, and 23.4. Highest TN removal efficiency of 77.2% was achieved at C/N and N/P ratios of 12.2 and 2.0, respectively. Highest PO43- removal of 78.8% was obtained at N/P ratio 12.8. However, C/N ratio did not affect the removal efficiency. Maintaining these specified C/N and N/P ratios in the influent greywater would ensure that the treated greywater meets the required standards for various reuse applications, including flushing, groundwater recharge, and surface water discharge. The integration of RSM with AdaBoost and XGBoost provided accurate predictions of removal efficiencies. For all the models, XGBoost had the highest R2, and lowest MAE and MSE values. The cross validation of RSM models with AdaBoost and XGBoost further reinforced the reliability of these models in predicting treatment outcomes.