Smart solutions for clean air: An AI-guided approach to sustainable industrial pollution control in coal-fired power plant.

Conventional fossil fuels are relied on heavily to meet the ever-increasing demand for energy required by human activities. However, their usage generates significant air pollutant emissions, such as NOx, SOx, and particulate matter. As a result, a complete air pollutant control system is necessary. However, the intensive operation of such systems is expected to cause deterioration and reduce their efficiency. Therefore, this study evaluates the current air pollutant control configuration of a coal-powered plant and proposes an upgraded system. Using a year-long dataset of air pollutants collected at 30-min intervals from the plant's telemonitoring system, untreated flue gas was reconstructed with a variational autoencoder. Subsequently, a superstructure model with various technology options for treating NOx, SOx, and particulate matter was developed. The most sustainable configuration, which included reburning, desulfurization with seawater, and dry electrostatic precipitator, was identified using an artificial intelligence (AI) model to meet economic, environmental, and reliability targets. Finally, the proposed system was evaluated using a Monte Carlo simulation to assess various scenarios with tightened discharge limits. The untreated flue gas was then evaluated using the most sustainable air pollutant control configuration, which demonstrated a total annual cost, environmental quality index, and reliability indices of 44.1 × 106 USD/year, 0.67, and 0.87, respectively.

Blockchain as a frontier in biotechnology and bioenergy applications.

The fourth Industrial Revolution is stimulating a fast-paced and resilient industrial internet of things (IIoT) ecosystem. Blockchain, a decentralized digital ledger technology, plays a crucial role in improvising, securing, and streamlining traditional biotechnology-related industrial processes with IoT and creates a sustainable nexus between social, economic, and environmental aspects.

Cultivation of Chlorella vulgaris on dairy waste using vision imaging for biomass growth monitoring.

Continuous automation of conventional industrial operations with smart technology have drawn significant attention. Firstly, the study investigates on optimizing the proportion of industrial biscuit processing waste powder, (B) substituted into BG-11 as a source of cultivation medium for the growth of C. vulgaris. Various percentages of industrial biscuit processing waste powder, (B) were substituted in the inorganic medium to analyse the algal growth and biochemical composition. The use of 40B combination was found to yield highest biomass concentration (4.11 g/L), lipid (260.44 mg/g), protein (263.93 mg/g), and carbohydrate (418.99 mg/g) content compared with all the other culture ratio combination. Secondly, the exploitation of colour acquisition was performed onto C. vulgaris growth phases, and a novel photo-to-biomass concentration estimation was conducted via image processing for three different colour model pixels. Based on linear regression analysis the red, green, blue (RGB) colour model can interpret its colour variance precisely.

Inter-regional multimedia fate analysis of PAHs and potential risk assessment by integrating deep learning and climate change scenarios.

Polycyclic aromatic hydrocarbons (PAHs) are hazardous compounds associated with respiratory disease and lung cancer. Increasing fossil fuel consumption, which causes climate change, has accelerated the emissions of PAHs. However, potential risks by PAHs have not been predicted for Korea, and appropriate PAH regulations under climate change have yet to be developed. This study assesses the potential risks posed by PAHs using climate change scenarios based on deep learning, and a multimedia fugacity model was employed to describe the future fate of PAHs. The multimedia fugacity model describes the dynamics of sixteen PAHs by reflecting inter-regional meteorological transportation. A deep neural network predicts future environmental and economic conditions, and the potential risks posed by PAHs, in the year 2050, using a prediction model and climate change scenarios. The assessment indicates that cancer risks would increase by more than 50%, exceeding the lower risk threshold in the southern and western regions. A mix of strategies for developing PAH regulatory policies highlighted the necessity of increasing PAHs monitoring stations and controlling fossil fuel usage based on the domestic and global conditions under climate change scenarios.

Synthesis, characterization and application of 1-butyl-3 methylimidazolium chloride as green material for extractive desulfurization of liquid fuel.

The possible application of imidazolium ionic liquids as energy-efficient green material for extractive deep desulfurization of liquid fuel has been investigated. 1-Butyl-3-methylimidazolium chloride [BMIM]Cl was synthesized by nucleophilic substitution reaction of n-methylimidazolium and 1-chlorobutane. Molecular structures of the ILs were confirmed by FTIR, (1)H-NMR, and (13)C-NMR. The thermal properties, conductivity, solubility, water content and viscosity analysis of [BMIM]Cl were carried out. The effects of reaction time, reaction temperature, sulfur compounds, and recycling of IL without regeneration on dibenzothiophene removal of liquid fuel were presented. In the extractive desulfurization process, the removal of dibenzothiophene in n-dodecane using [BMIM]Cl was 81% with mass ratio of 1 : 1, in 30 min at 30°C under the mild reaction conditions. Also, desulfurization of real fuels with IL and multistage extraction were studied. The results of this work might offer significant insights in the perceptive use of imidazoled ILs as energy-efficient green material for extractive deep desulfurization of liquid fuels as it can be reused without regeneration with considerable extraction efficiency.

Thiolated glycol chitosan bearing α-cyclodextrin for sustained delivery of PEGylated human growth hormone.

The extensive use of human growth hormone (hGH), emerging as protein therapeutics, has been limited by its instability in biological fluids and short biological half-life. In this study, thiolated glycol chitosan bearing α-cyclodextrin (TGC-CD), in situ cross-linked by poly(ethylene glycol)-diacrylate (PEG-DA), was synthesized to develop a sustained release system for PEGylated hGH (PEG-hGH). TGC-CD could form a stable inclusion complex with PEG-hGH by the physical interaction between the inner cavity of CD and PEG. Such a complex was readily cross-linked in the presence of PEG-DA via a Michael-type addition reaction. From the in vitro release experiments of PEG-hGH, it was confirmed that PEG-hGH was completely released from the complex for 12 h in PBS (pH 7.4), whereas the release rate of PEG-hGH was significantly reduced by the chemical cross-linking of the complex. PEG-hGH, released from the cross-linked complexes, maintained its structural integrity, which was demonstrated using circular dichroism spectroscopy. Overall, TGC-CD might be useful for sustained delivery of PEG-hGH.

Bioreducible block copolymers based on poly(ethylene glycol) and poly(γ-benzyl L-glutamate) for intracellular delivery of camptothecin.

Poly(ethylene glycol)-b-poly(γ-benzyl L-glutamate)s bearing the disulfide bond (PEG-SS-PBLGs), which is specifically cleavable in intracellular compartments, were prepared via a facile synthetic route as a potential carrier of camptothecin (CPT). Diblock copolymers with different lengths of PBLG were synthesized by ring-opening polymerization of benzyl glutamate N-carboxy anhydride in the presence of a PEG macroinitiator (PEG-SS-NH(2)). Owing to their amphiphilic nature, the copolymers formed spherical micelles in an aqueous condition, and their particle sizes (20-125 nm in diameter) were dependent on the block length of PBLG. Critical micelle concentrations of the copolymers were in the range 0.005-0.065 mg/mL, which decreased as the block length of PBLG increased. CPT, chosen as a model anticancer drug, was effectively encapsulated up to 12 wt % into the hydrophobic core of the micelles by the solvent casting method. It was demonstrated by the in vitro optical imaging technique that the fluorescence signal of doxorubicin, quenched in the PEG-SS-PBLG micelles, was highly recovered in the presence of glutathione (GSH), a tripeptide reducing disulfide bonds in the cytoplasm. The micelles released CPT completely within 20 h under 10 mM GSH, whereas only 40% of CPT was released from the micelles in the absence of GSH. From the in vitro cytotoxicity test, it was found that CPT-loaded PEG-SS-PBLG micelles showed higher toxicity to SCC7 cancer cells than CPT-loaded PEG-b-PBLG micelles without the disulfide bond. Microscopic observation demonstrated that the disulfide-containing micelle could effectively deliver the drug into nuclei of SCC7 cells. These results suggest that PEG-SS-PBLG diblock copolymer is a promising carrier for intracellular delivery of CPT.

Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor.

Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and so on. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearity; (2) multiple models with a posterior probability for modeling different operating regions; (3) local batch monitoring by the T(2)- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then, these local regions can be supervised separately, leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model.

Integrated framework of nonlinear prediction and process monitoring for complex biological processes.

Bioprocesses and biosystems have nonlinear and multiple operation patterns depending on the influent loads, temperatures, the activity of microorganisms, and other factors. In this paper, an integrated framework of nonlinear modeling and process monitoring methods is developed for a complex biological process. The proposed method is based on modeling by fuzzy partial least squares (FPLS) and on process monitoring by a statistical decomposition, which is suitable for predicting and supervising a nonlinear biological process. Case studies in the bio-simulated process and industrial biological plant show that the proposed method can give superior prediction and monitoring performance in complex biological plants compared to other linear and nonlinear methods, since it can effectively capture the nonlinear causal relationship within the biosystem. This gives us the integrated framework that is able to both model and monitor the nonlinear bioprocess simultaneously.

On-line adaptive and nonlinear process monitoring of a pilot-scale sequencing batch reactor.

This article describes the application of on-line nonlinear monitoring of a sequencing batch reactor (SBR). Three-way batch data of SBR are unfolded batch-wisely, and then a adaptive and nonlinear multivariate monitoring method is used to capture the nonlinear characteristics of normal batches. The approach is successfully applied to an 80 L SBR for biological wastewater treatment, where the SBR poses an interesting challenge in view of process monitoring since it is characterized by nonstationary, batchwise, multistage, and nonlinear dynamics. In on-line batch monitoring, the developed adaptive and nonlinear process monitoring method can effectively capture the nonlinear relationship among process variables of a biological process in a SBR. The results of this pilot-scale SBR monitoring system using simple on-line measurements clearly demonstrated that the adaptive and nonlinear monitoring technique showed lower false alarm rate and physically meaningful, that is, robust monitoring results.

Enhanced process monitoring of fed-batch penicillin cultivation using time-varying and multivariate statistical analysis.

On-line monitoring of penicillin cultivation processes is crucial to the safe production of high-quality products. In the past, multiway principal component analysis (MPCA), a multivariate projection method, has been widely used to monitor batch and fed-batch processes. However, when MPCA is used for on-line batch monitoring, the future behavior of each new batch must be inferred up to the end of the batch operation at each time and the batch lengths must be equalized. This represents a major shortcoming because predicting the future observations without considering the dynamic relationships may distort the data information, leading to false alarms. In this paper, a new statistical batch monitoring approach based on variable-wise unfolding and time-varying score covariance structures is proposed in order to overcome the drawbacks of conventional MPCA and obtain better monitoring performance. The proposed method does not require prediction of the future values while the dynamic relations of data are preserved by using time-varying score covariance structures, and can be used to monitor batch processes in which the batch length varies. The proposed method was used to detect and identify faults in the fed-batch penicillin cultivation process, for four different fault scenarios. The simulation results clearly demonstrate the power and advantages of the proposed method in comparison to MPCA.

Application of multiway ICA for on-line process monitoring of a sequencing batch reactor.

Multiway principal component analysis has been shown to be a powerful monitoring tool in many industrial batch processes. However, it has the shortcomings that all batch lengths should be equal, the measurement variables must be normally distributed and that future values of the current batch must be estimated to allow on-line monitoring. In this work, it is shown that multiway independent component analysis (MICA) can be used to overcome these drawbacks and obtain better monitoring performance. The on-line MICA monitoring of batch processes is based on a new unfolding method and independent component analysis (ICA). ICA provides better monitoring performance than PCA in cases with non-Gaussian data because it is not based on the assumption that the latent variables are normally distributed. The MICA algorithm does not require any estimation of future batch values and can also be applied to non-equal batch length data sets. This article describes the application of on-line MICA monitoring of a sequencing batch reactor (SBR). It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of disturbance sources with non-Gaussian characteristics. The SBR poses an interesting challenge from the point of process monitoring characterized by non-stationary, batchwise, multiscale, and non-Gaussian characteristics. The results of the bench-scale SBR monitoring clearly showed the power and advantages of MICA monitoring in comparison to conventional monitoring methods.

Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical methods in biological wastewater treatment plants.

A new approach to nonlinear modeling and adaptive monitoring using fuzzy principal component regression (FPCR) is proposed and then applied to a real wastewater treatment plant (WWTP) data set. First, principal component analysis (PCA) is used to reduce the dimensionality of data and to remove collinearity. Second, the adaptive credibilistic fuzzy-c-means method is used to appropriately monitor diverse operating conditions based on the PCA score values. Then a new adaptive discrimination monitoring method is proposed to distinguish between a large process change and a simple fault. Third, a FPCR method is proposed, where the Takagi-Sugeno-Kang (TSK) fuzzy model is employed to model the relation between the PCA score values and the target output to avoid the over-fitting problem with original variables. Here, the rule bases, the centers and the widths of TSK fuzzy model are found by heuristic methods. The proposed FPCR method is applied to predict the output variable, the reduction of chemical oxygen demand in the full-scale WWTP. The result shows that it has the ability to model the nonlinear process and multiple operating conditions and is able to identify various operating regions and discriminate between a sustained fault and a simple fault (or abnormalities) occurring within the process data.