The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.

The application of microalgae in applied biotechnological studies for different bio-materials, such as biodiesel, bioethanol, and other high-value bioproducts, has been gaining attention in recent years. Large-scale integrated microalgae-wastewater treatment facilities have emerged as a promising technology. Technoeconomic and life cycle analyses of integrated algae technology in municipal wastewater treatment plants (WWTPs) can reveal its potential as a viable market technology. Thus, integrated microalgae WWTPs is seen as a promising field and is getting attention from the scientific community due to its multifold benefits in terms of nitrogen and phosphorous removal with reduction of organic load, accumulation of heavy metals, and simultaneous production of value-added biomaterials. This chapter was designed to provide concise details on recent advancements in biological and technological approaches, LCA studies, and IoT and edge computing-based modeling and monitoring of integrated microalgae WWTPs with a technoeconomic feasibility analysis for its assessment as a promising market technology. It is noteworthy that stakeholders have an interest in integrated microalgae WWTPs, but are looking for a standardized process, including design, data availability, and management aspects, along with a legislative framework that makes it simple to implement.

Soil pollution with emerging contaminants such as human and veterinary pharmaceuticals, antibiotics, steroids, endocrine disruptors, perfluorinated compounds, water disinfection by-products, gasoline, industrial additives, and microplastics is one of the most persistent environmental problems, which poses a serious threat to the humans and the environment. Phytoremediation, one of the innovative strategies for remediating the soil polluted by such emerging contaminants, has been recognized as a powerful in situ approach to soil remediation. The synergistic actions of plants and their associated microorganisms can improve plant growth and enhance the biodegradation of emerging contaminants, thereby accelerating the removal of these pollutants from the soil. In view of the aforementioned discussion, this book chapter is designed to cover the plant species demonstrating higher removal efficiency of emerging contaminants from soil, explain different factors influencing phytoremediation of emerging contaminants in soil, and discuss the different fundamental mechanisms of endophyte-assisted phytoremediation of emerging contaminants. Finally, the advances, challenges, and new directions in the field of phytoremediation technology for the removal of selected emerging contaminants are also discussed.

This study is aimed at utilizing three waste materials, i.e., solid refuse fuel (SRF), tire derived fuel (TDF), and sludge derived fuel (SDF), as eco-friendly alternatives to coal-only combustion in co-firing power plants. The contribution of waste materials is limited to ≤5% in the composition of the mixed fuel (coal + waste materials). Statistical experimental design and response surface methodology are employed to investigate the effect of mixed fuel composition (SRF, TDF, and SDF) on gross calorific value (GCV) and ash fusion temperature (AFT). A quadratic model is developed and statistically verified to apprehend mixed fuel constituents' individual and combined effects on GCV and AFT. Constrained optimization of fuel blend, i.e., GCV >1,250 kcal/kg and AFT >1,200 °C, using the polynomial models projected the fuel-blend containing 95% coal with 3.84% SRF, 0.35% TDF, and 0.81% SDF. The observed GCV of 5,307 kcal/kg and AFT of 1225 °C for the optimized blend were within 1% of the model predicted values, thereby establishing the robustness of the models. The findings from this study can foster sustainable economic development and zero CO2 emission objectives by optimizing the utilization of waste materials without compromising the GCV and AFT of the mixed fuels in coal-fired power plants.

Ultrasonic treatment is adopted as one of the important strategies to accelerate the hydrolysis of waste-activated sludge (WAS). This study intends to optimize amplitude and time for ultrasonic treatment of WAS using statistically designed experiments that would render a better degree of disintegration, measured in terms of chemical oxygen demand (COD) solubilization. The main and interaction effects of nonlinear regression analysis revealed a significant interaction between amplitude and time over the degree of sludge disintegration. The ultrasonic density of 0.45 W/mL for 30 min favored a 153.84% increase in COD solubilization with a 27.65% degree of sludge disintegration. This condition also favored the minimum specific energy input of 801.58 kJ/g total suspended solids compared to other sonication conditions. Besides, the sludge volume index was reduced by 45% and 26% at the ultrasonic density of 0.45 W/mL for 60 and 30 min, respectively. Microscopic examination of WAS revealed disintegration of bacterial flocs during the ultrasonication process releasing filamentous bacteria with smaller floc sizes. The results from this study indicate that higher ultrasonic density for short time could improve the sludge disintegration with minimum specific energy input.

This study investigates chromium removal onto modified maghemite nanoparticles in batch experiments based on a central composite design. The effect of modified maghemite nanoparticles on the adsorptive removal of chromium was quantitatively elucidated by fitting the experimental data using artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modeling approaches. The ANN and ANFIS models, relating the inputs, i.e., pH, adsorbent dose, and initial chromium concentration to the output, i.e., chromium removal efficiency (RE), were developed by comparing the predicted value with that of the experimental values. The RE of chromium ranged from 49.58% to 92.72% under the influence of varying pH (i.e., 2.6–9.4) and adsorbent dose, i.e., 0.8 g/L to 9.2 g/L. The developed ANN model fits the experimental data exceptionally well with correlation coefficients of 1.000 and 0.997 for training and testing, respectively. In addition, the Pearson’s Chi-square measure (χ2) of 0.0004 and 0.0673 for the ANN and ANFIS models, respectively, indicated the superiority of ANN over ANFIS. However, a small discrepancy in the predictability of the ANFIS model was observed owing to the fuzzy rule-based complexity and overtraining of data. Thus, the developed models can be used for the online prediction of RE onto synthesized maghemite nanoparticles with different sets of input parameters and it can also predict the operational errors in the system.

Almost 40 years since Eric Drexler’s 1991 MIT dissertation, “Molecular Machinery and Manufacturing with Applications to Computation” (Drexler, Nanosystems: molecular machinery, manufacturing, and computation, Wiley, 1992), a lot of exciting has happened in nanotechnology. By far, this nascent field of technology in combination with biotechnology has contributed heavily in medicine and biomaterials R&D sector, but nanomaterials for “green applications” are still quite away from realization. There are several issues underlying this delay, and these can be classified into two broad categories: (i) issues intrinsic to nanomaterials: associated with sustainable tailor-made production, optimization, scale-up, and stability and (ii) environmental issues of nanomaterials associated with their interaction and safe disposal. To have further clarity on first category above, the major intrinsic issues of nanomaterials are grouped into six attributes of studies. These are composition, synthesis, internal and external properties, stability, toxicity, and lifecycle assessment. Although these appear independent attributes but actually they interact with each other and a term “Ensemble Heterogeneity” (he) is defined here to understand their interdependence. Machine learning (ML) can provide deeper insights into both visible and hidden levels of these interdependencies. In the present chapter, firstly, these six attributes are reviewed and an understanding is developed that these attributes must be studied for hidden parameters and patterns using ML for producing optimized design solutions for nanomaterials. Secondly, an overview of different types of ML approaches is given that can contribute to the development of optimized design of nanomaterials. Finally, life cycle assessment (LCA) of nanomaterials is discussed. LCA is a tool to assess the environmental impact and sustainability of any technology to be “green.”

Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) adsorption onto Zn-loaded pinecone (PC) biochar through batch experiments conducted under Box–Behnken design. The effect of EtOH on As(III) adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtOH and pH on As(III) adsorption, whereas neural network revealed the stronger influence of EtOH (64.5%) followed by pH (20.75%) and As(III) concentration (14.75%) on the adsorption phenomena. Besides, the interaction among process variables indicated that EtOH enhances As(III) adsorption over a pH range of 2 to 7, possibly due to facilitation of ligand–metal(Zn) binding complexation mechanism. Eventually, hybrid response surface model–genetic algorithm (RSM–GA) approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As(III) (10.47 μg/g) is facilitated at 30.22 mg C/L of EtOH with initial As(III) concentration of 196.77 μg/L at pH 5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As(III) species in the presence of OM.

Polyhydroxyalkanoates (PHAs) are bioplastics that naturally accumulate in the microbial cells while performing organic substrate metabolism. PHAs bioconversion in microbial cells is affected by both growth environments (aerobic and anaerobic) and feeding systems (carbon and nutrient limitations). Sequential batch reactors (SBRs) were used in this research for producing PHAs; studies showed on an average 42% with a maximum of 63% PHAs yield under anaerobic-oxic conditions quantified by gas chromatography. Produced PHA5 from phase 4 were thermally and physically characterized. Fourier transform infrared spectroscopy showed strong presence of carbonyl peaks at 1720 cm(-1) in all PHAs. Gel permeation chromatography reported polydipersity index values in range of 2.4-3.6 showing non-uniformity of molecular weights in the slice of PHAs and differential scanning calorimetry reported melting point temperatures of 146-154 degrees C, confirming usefulness of produced PHAs in industrial applications. X-Ray Diffraction confirmed crystal structure in all PHAs with the most crystalline from SBR3. Thermogravimetric analysis further confirmed highest thermal degradation temperature of 283 degrees C for PHAs from SBR3. Different blends of wastewater fed to mixed sodium acetate acclimatized biomass further showed the importance of substrate carbon source for PHAs production in various growth environments. (c) 2016 Elsevier B.V. All rights reserved.

Polyhydroxyalkanoates (PHAs) are bioplastics that naturally accumulate in the microbial cells while performing organic substrate metabolism. PHAs bioconversion in microbial cells is affected by both growth environments (aerobic and anaerobic) and feeding systems (carbon and nutrient limitations). Sequential batch reactors (SBRs) were used in this research for producing PHAs; studies showed on an average 42% with a maximum of 63% PHAs yield under anaerobic–oxic conditions quantified by gas chromatography. Produced PHAs from phase 4 were thermally and physically characterized. Fourier transform infrared spectroscopy showed strong presence of carbonyl peaks at 1720 cm−1 in all PHAs. Gel permeation chromatography reported polydipersity index values in range of 2.4–3.6 showing non-uniformity of molecular weights in the slice of PHAs and differential scanning calorimetry reported melting point temperatures ◦ erobic processes naerobic processes ioconversion atch processing of 146–154 C, confirming usefulness of produced PHAs in industrial applications. X-Ray Diffraction confirmed crystal structure in all PHAs with the most crystalline from SBR3. Thermogravimetric analysis further confirmed highest thermal degradation temperature of 283 ◦C for PHAs from SBR3. Different blends of wastewater fed to mixed sodium acetate acclimatized biomass further showed the importance of substrate carbon source for PHAs production in various growth environments. © 2016 Elsevier B.V. All rights reserved.

This study investigates the anaerobic biodegradation characteristics of a simulated Bangladeshi food waste. Biochemical methane potential experiments carried out in 500 mL batch digesters showed the methane yields of cooked meat, cooked fish, boiled rice, vegetable and mixed food waste (MFW) to be 541, 402, 319, 274 and 484 ml CH4/gVS, respectively. The biodegradability of 87%, 83%, 75%, 76% and 82% were obtained for cooked meat, cooked fish, boiled rice, vegetable and MFW, respectively. At mesophilic temperature (35 degrees C), a maximum of 484.41 ml CH4/g VS was observed at an inoculum to substrate ratio (ISR) of 1 followed by 280, 166, 194 and 43 mL CH4/g VS at ISRs 10, 2, 0.1 and 0.05, respectively. The modified Gompertz model utilised for estimating the kinetic parameters of methane production revealed the maximum methane production rate as 16.02 ml CH4/g VS.d at an ISR of 1 with lag phase of 3.8 days.

The potential of raw pine cone (PC) biochar and its modified form (Zn-loaded biochar) was evaluated for the removal of trivalent arsenic [As(III)] from the aqueous solution. The influence of treatment of PC biochar with Zn(NO 3 ) 2 on the physico-chemical properties of biochar was examined using elemental analyzer, surface area analyzer, thermo-gravimetric analyzer, Fourier transform infrared spectroscopy, and scanning electron microscopy. Experimental results revealed that the removal of As(III) was almost consistent (66.08 ± 3.94 and 87.62 ± 3.88 % on raw and Zn-loaded biochar, respectively) over an acidic pH range of 2–4 as a consequence of high affinity between the positively charged biochar surface and the predominant arsenic species (H 3 AsO 3 and H 2 AsO 4 − ), followed by a decrease with increase in pH in the range of 4–12. Langmuir isotherm model was well fitted to the experimental equilibrium data ( R 2 = 0.93) rendering the maximum adsorption capacity of 5.7 and 7.0 μg g −1 on raw and Zn-loaded PC biochar, respectively. The adsorption of As(III) was well represented by pseudo-second order with reaction rate constant ( k 2 ) of 0.040 and 0.282 mg g −1 min on raw and zinc-loaded PC biochar, respectively, and was mainly controlled by boundary layer diffusion followed by some extent of intra-particle diffusion. The adsorption process was spontaneous with Gibb’s free energy (Δ G °, −4.42 ± 0.72 and −11.86 ± 1.78 kJ mol −1 ) and exothermic in nature with enthalpy (Δ H °, 13.25 and 31.10 kJ mol −1 ) and entropy (Δ S °, 0.058 and 0.141 kJ mol −1 K −1 ) for raw and zinc-loaded adsorbent, respectively.

The dynamics of microbial growth and poly(3-hydroxybutyrate) [P(3HB)] production in growth/ non-growth phases of Azhohydromonas lata MTCC 2311 were studied using a maintenance-energy-dependent mathematical model. The values of calculated model kinetic parameters were: ms1 = 0.0005 h-1, k = 0.0965, µmax = 0.25 h-1 for glucose; ms1 = 0.003 h-1, k = 0.1229, µmax = 0.27 h-1 for fructose; and ms1 = 0.0076 h-1, k = 0.0694, µmax = 0.25 h-1 for sucrose. The experimental data of biomass growth, substrate consumption, and P(3HB) production on different carbon substrates were mathematically fitted using non-linear least square optimization technique and similar trends, but different levels were observed at varying initial carbon substrate concentration. Further, on the basis of substrate assimilation potential, cane molasses was used as an inexpensive and renewable carbon source for P(3HB) production. Besides, the physico-chemical, thermal, and material properties of synthesized P(3HB) were determined which reveal its suitability in various applications.

In this study, the feasibility of glycerol valorization into homo-and hetero-polymers of polyhydroxyalkanoates by a sludge isolated Bacillus sp. RER002 in a 3 L bioreactor was investigated. A mathematical model including logistic, Luedeking-Piret, and Luedeking-Piret-like equations that simulated the active residual biomass growth, P(3HB) synthesis, and glycerol consumption, respectively, was developed. In order to describe the dynamics of batch P(3HB) production, the model kinetic parameters viz., mu max, K-1, K-2, alpha, beta, and K-N were optimized using the stochastic searchbased genetic algorithm. The synthesis of P(3HB) was observed to be highly growth associated and partially non-growth associated as reflected in a significant higher values of K-1 (0.2435-0.5477) than K-2 (2.2 x 10(-6) to 9.1 x 10(-3)) within the glycerol concentration range of 10(-40) g/L. Besides, the maximum 3.2 g/L of copolymer [P(3HAscl-co-3HAmcl)] was observed at 30 g/L of glycerol concentration in synthetic crude glycerol medium with a yield coefficient (YP/S) of 0.16 g/g. Furthermore, the analyses of chemical and thermal properties of copolymer P(3HA(scl)co-3HA(mcl)) revealed its enhanced material properties which make it suitable for various applications.

Optimal biogas production and sludge treatment were studied by co-digestion experiments and modeling using five different wastewater sludges generated from paper, chemical, petrochemical, automobile, and food processing industries situated in Ulsan Industrial Complex, Ulsan, South Korea. The biomethane production potential test was conducted in simplex-centroid mixture design, fitted to regression equation, and some optimal co-digestion scenarios were given by combined desirability function based multi-objective optimization technique for both methane yield and the quantity of sludge digested. The co-digestion model incorporating main and interaction effects among sludges were utilized to predict the maximum possible methane yield. The optimization routine for methane production with different industrial sludges in batches were repeated with the left-over sludge of earlier cycle, till all sludges have been completely treated. Among the possible scenarios, a maximum methane yield of 1161.53 m(3) is anticipated in three batches followed by 1130.33 m(3) and 1045.65 m(3) in five and two batches, respectively. This study shows a scientific approach to find a practical solution to utilize diverse industrial sludges in both treatment and biogas production perspectives.

The characteristics and impact of industrial sludges of paper, chemical, petrochemical, automobile, and food industries situated in the Ulsan Industrial Complex, Ulsan, Republic of Korea in co-digestion for biogas production were assessed by artificial neural network (ANN) and statistical regression models. The regression model was based on a simplex-centroid mixture design and the ANN was based on a resilient back-propagation algorithm (topology 5-7-1). Using connection weights and bias of the trained ANN model, the impact of each sludge of co-digestion was assessed using Garsons’ algorithm. Results suggested that the modelling and predictability of ANN were superior to the regression model with accuracy (A f) 1.01, bias (B f) 1.00, root mean square error 3.56, and standard error of prediction 2.51%. Sludge from the chemical industry showed the highest impact on specific methane yield (SMYvs) with a relative importance of 28.59% followed by sludges from paper (20.07%), food (19.59%), petrochemical (15.92%), and automobile (15.82%) industries. The interactions between diverse industrial sludges were successfully modelled and partitioned into various synergistic and antagonistic effects on SMYvs. Synergistic interactions between the chemical industry sludge and either petrochemical or food industry sludges on SMYvs were detected. However, strong negative interaction between automobile sludge and other sludges was observed. This study indicates that though the ANN model performed better in prediction and impact assessments, the regression model reveals the synergistic and antagonistic interactions among sludges.

The feasibility of a thermal liquid-phase oxidation process has been tested to treat the organic content of industrial waste water derived from chemical industries. The effect of the operating parameters on COD removal including temperature, pressure, pH, and residence time was studied. The experiments were conducted in a stainless steel autoclave reactor and the maximum of 49.77% of COD removal was achieved after 120 min of reaction time at 2300C temperature and pH 7.9, indicating a significant improvement in degradability of organic effluent. An ideal pressure of 28 Kg/cm2 was observed for maximum COD removal of 43.18%. This liquid-phase oxidation technique shows the significant potential for the treatment of organic load of industrial wastewater.

The feasibility of a catalytic liquid–phase oxidation process using copper sulphate to treat the organic pollutant of industrial wastewater derived from a chemical industry. The effect of the operating parameters such as temperature, pressure, pH, and residence time on COD removal efficiency was studied. The experiments were conducted in a stainless steel autoclave reactor and the maximum of 91.61% of COD removal was achieved after 150 min of reaction time at 230 0 C temperature and 34 kg/cm 2 pressure, resulting a significant improvement in organic effluent discharge of industry. An ideal pressure of 31 kg/cm 2 in reactor was determined that favoured the maximum COD removal of 78.80%. Here, the copper sulphate catalyst was found as an effective homogeneous catalyst for maximum degradation of organic pollutants. This catalytic liquid-phase oxidation technique shows the significant potential for the treatment of organic content of industrial wastewater.