Artificial Intelligence driven smart operation of large industrial complexes supporting the net-zero goal: Coal power plants

The true potential of artificial intelligence (AI) is to contribute towards the performance enhancement and informed decision making for the operation of the large industrial complexes like coal power plants. In this paper, AI based modelling and optimization framework is developed and deployed for the smart and efficient operation of a 660 MW supercritical coal power plant. The industrial data under various power generation capacity of the plant is collected, visualized, processed and subsequently, utilized to train artificial neural network (ANN) model for predicting the power generation. The ANN model presents good predictability and generalization performance in external validation test with R2 = 0.99 and RMSE =2.69 MW. The partial derivative of the ANN model is taken with respect to the input variable to evaluate the variable’ sensitivity on the power generation. It is found that main steam flow rate is the most significant variable having percentage significance value of 75.3 %. Nonlinear programming (NLP) technique is applied to maximize the power generation. The NLP-simulated optimized values of the input variables are verified on the power generation operation. The plant-level performance indicators are improved under optimum operating mode of power generation: savings in fuel consumption (3 t/h), improvement in thermal efficiency (1.3 %) and reduction in emissions discharge (50.5 kt/y). It is also investigated that maximum power production capacity of the plant is reduced from 660 MW to 635 MW when the emissions discharge limit is changed from 510 t/h to 470 t/h. It is concluded that the improved plant-level performance indicators and informed decision making present the potential of AI based modelling and optimization analysis to reliably contribute to net-zero goal from the coal power plant

Machine learning based modelling and optimization of post-combustion carbon capture process using MEA supporting carbon neutrality

The role of carbon capture technology using monoethanolamine (MEA) is critical for achieving the carbon-neutrality goal. However, maintaining the efficient operation of the post-combustion carbon capture is challenging considering the hyperdimensional design space and nonlinear characteristics of the process. In this work, CO2 capture level from the flue gas in the absorption column is investigated for the post-combustion carbon capture process using MEA. Artificial neural network (ANN) and support vector machine (SVM) models are constructed to model CO2 capture level under extensive hyperparameters tuning. The comparative performance analysis based on external validation test confirmed the superior modelling and generalization ability of ANN for the carbon capture process. Later, partial derivative-based sensitivity analysis is carried out and it is the found that absorbent-based input variables like lean solvent temperature and lean solvent flow rate are the two most significant input variables on CO2 capture level in the absorption column. The optimization problem with the ANN model embedded in the nonlinear programming-based optimization environment is solved under different operating scenarios to determine the optimum operating ranges for the input variables corresponding to the maximum CO2 capture level. This research presents the optimum operating conditions for CO2 removal from the flue gas for the post-combustion carbon capture process using MEA that contributes to achieving the carbon neutrality goal

BK channel modulation as a theraputic target for Cystic Fibrosis

Background. Cystic fibrosis and is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an anion channel present on the apical membrane of airway epithelia. Loss of CFTR function reduces anion secretion into the airway surface liquid (ASL), causing ASL dehydration and an impairment of the innate immune response. CFTR modulator drugs seek to increase the number of functional channels at the cell membrane and the CFTR open probability. However, the apical membrane potential sits close to the reversal potential for chloride. Thus, physiologically, it seems that the co-activation of potassium channels is necessary to provide the driving force for anion secretion. Therefore, another way to increase anion secretion is to increase activation of these channels. One such target is the large conductance calcium-activated potassium channel (BK channel) [1] that also resides on the apical membrane of human bronchial epithelium. / Aim. To investigate the role of BK channels both theoretically and experimentally. / Theory. We first examined BK channel modulation by extending a model of ion transport in epithelial cells first developed by O’Donoghue et al. [2]. The extended model included an ASL compartment where depth and ion concentrations could be predicted. / Experiment. We next tested BK channel activation experimentally using CF-donor cells carrying R334W/ΔF508 CFTR mutation. Cells were grown in PneumaCultTM-Ex Plus and PneumaCult™-ALI Medium to create air-liquid interface (ALI) cultures in the absence of antibiotics and antimycotic agents. We used BK channel activator GoSlo-SR-5-6 [4] (GoSlo) to modulate the BK channel. GoSlo lowers the voltage required for half maximal activation for all BK channels, independent of their subunit composition, by about 50 mV, allowing them to stay open at relatively hyperpolarised potentials. To measure ASL depth we used the method of Ivanova et al. [3]. We investigated the effect on ASL depth on HBE cultures by measuring a control with DMSO (vehicle) applied to the basolateral side of the culture vs GoSlo (10 uM), also applied basolaterally or GoSlo applied first vs DMSO only 24 hrs later. / Results and conclusion. A simple theoretical model of ion transport in airway epithelia predicts that activation of basolateral BK channels will increase airway hydration, in agreement with data published by Manzanares and colleagues [1]. This prediction is well-matched by experiment which shows a ~77% increase in ASL depth from the DMSO baseline. BK channel activation may thus be therapeutically useful in cystic fibrosi

Modelling the role of ion transport in controlling airway surface liquid

ATP and protease activity in the airway surface liquid (ASL), are thought to control ASL depth. Many experiments have examined this control system by measuring absorption rates when excess fluid is added to the ASL. However, these experiments often use saline solutions that are not well matched to the ASL ion composition. We have developed a simple mathematical model of ion transport (Figure 1) and simulated the impact of changing ion composition alone without any ASL regulatory pathways

Sustainable EDM of Inconel 600 in Cu-mixed biodegradable dielectrics: Modelling and optimizing the process by artificial neural network for supporting net-zero from industry

The properties of Nickel-based superalloy(s) like stability at extreme conditions, greater strength, etc., complicate its cutting through conventional operations. Therefore, electric discharge machining (EDM) is preferred for its accurate cutting. However, the conventional dielectric i.e., kerosene used in EDM is hydrocarbon based which generates toxic fumes and contribute to the CO2 emissions during the discharging process in EDM. This affects the operator’s health and the environment. Therefore, the potentiality of five biodegradable dielectrics has been deeply examined herein to address the said issues. Nano copper powder is also employed for uplifting the cutting proficiency of these dielectrics. A set of 15 experiments was performed via full factorial design. An artificial neural network (ANN) is constructed to model and optimize the material removal rate (MRR), surface roughness (SR), and specific energy consumption (SEC). The highest MRR (5.527 mm3 /min) was achieved in coconut oil whereas for obtaining the lowest SR, the sunflower oil at powder concentration (Cp) of 1.0 g/100 ml is the best choice. Sunflower oil also gave a 17.05% better surface finish compared to other dielectrics. Amongst the biodegradable dielectrics, olive oil consumes lowest specific energy (SEC) i.e., 264.16 J/mm3 which is 28.8% less than the SEC of other oils. Furthermore, the maximum CO2 reduction of 72.8 ± 1.4% is achieved with Olive oil in comparison to that found with kerosene in EDM. The multi-objective optimization is conducted and sunflower oil with Cp of 0.667 g/100 ml is termed out to be optimal solution. The biodegradable dielectrics have demonstrated excellent performance for EDM to support net-zero goals from the industrial sector

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

This work presents a Mixed Integer Linear Programming (MILP) approach based on a combination of a rolling horizon and stochastic programming formulation. The objective of the proposed formulation is the optimal management of the supply and demand of energy and heat in microgrids under uncertainty, in order to minimise the operational cost. Delays in the starting time of energy demands are allowed within a predefined time windows to tackle flexible demand profiles. This approach uses a scenario-based stochastic programming formulation. These scenarios consider uncertainty in the wind speed forecast, the processing time of the energy tasks and the overall heat demand, to take into account all possible scenarios related to the generation and demand of energy and heat. Nevertheless, embracing all external scenarios associated with wind speed prediction makes their consideration computationally intractable. Thus, updating input information (e.g., wind speed forecast) is required to guarantee good quality and practical solutions. Hence, the two-stage stochastic MILP formulation is introduced into a rolling horizon approach that periodically updates input information

Discrete Formulation for Multi-objective Optimal Design of Produced Water Treatment

Produced Water is naturally occurring water that is brought to the surface during the extraction of the oil and gas and it constitutes the largest waste stream in the oil and gas industry. In offshore platforms, the majority of the produced water is discharged into the ocean, threatening marine life. The treatment of produced water is attractive, not only to meet regulations but to secure a potential source of fresh water. The design of water treatment should consider economic, environmental, and social aspects. This paper presents a discrete model for the evolution of oil droplet distribution due to breakage and coalescence phenomena. The discrete model combined with a superstructure representation for process design results in a mixed integer non-linear program which is solved using a nature-inspired meta-heuristic optimization method

A game-theoretic optimisation approach to fair customer allocation in oligopolies

AbstractUnder the ever-increasing capital intensive environment that contemporary process industries face, oligopolies begin to form in mature markets where a small number of companies regulate and serve the customer base. Strategic and operational decisions are highly dependent on the firms’ customer portfolio and conventional modelling approaches neglect the rational behaviour of the decision makers, with regards to the problem of customer allocation, by assuming either static competition or a leader-follower structure. In this article, we address the fair customer allocation within oligopolies by employing the Nash bargaining approach. The overall problem is formulated as mixed integer program with linear constraints and a nonlinear objective function which is further linearised following a separable programming approach. Case studies from the industrial liquid market highlight the importance and benefits of the proposed game theoretic approach.</jats:p

Hydrodynamic effects on three phase micro-packed bed reactor performance – Gold–palladium catalysed benzyl alcohol oxidation

The hydrodynamics of a three-phase micro-packed bed reactor and its effect on catalysed benzyl alcohol oxidation with pure oxygen were studied in a silicon–glass microstructured reactor. The microreactor was operated at 120 °C and 1 barg and contained a channel with a 300 μm×600 μm cross-section, packed with 1 wt% Au–Pd/TiO2 catalyst, 65 μm in average diameter. Improvements in the conversion of benzyl alcohol and selectivity to benzaldehyde were observed with increasing gas-to-liquid ratio, which coincided with a change in the flow pattern from a liquid-dominated slug to a gas-continuous flow regime. The observed enhancement is attributed to improved external mass transfer, associated with an increase in the gas–liquid interfacial area and reduction in the liquid film thickness that occur with gradual changes in the flow pattern. A maximum selectivity of 93% to benzaldehyde was obtained under partial wetting – which introduced the added benefit of direct gas–solid mass transfer – outperforming the selectivity in a conventional glass stirred reactor. However, this was at the expense of a reduction in the conversion. A response surface model was developed and then used to predict optimal operating conditions for maximum benzaldehyde yield, which were in the gas-continuous flow regime. This corresponded to relatively high gas flow rate in conjunction with moderate liquid flow rate, ensuring sufficient catalyst wetting with a thin film to reduce transport resistance