Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm

Fed-batch fermentation has gained attention in recent years due to its beneficial impact in the economy and productivity of bioprocesses. However, the complexity of these processes requires an expert system that involves swarm intelligence-based metaheuristics such as Artificial Algae Algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Differential Evolution (DE) for simulation and optimization of the feeding trajectories. DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation. In this work, an improved version of DE namely Backtracking Search Algorithm (BSA) has edged DE and other recent metaheuristics to emerge as superior optimization method. This is shown by the results obtained by comparing the performance of BSA, DE, CMAES, AAA and ABC in solving six fed batch fermentation case studies. BSA gave the best overall performance by showing improved solutions and more robust convergence in comparison with various metaheuristics used in this work. Also, there is a gap in the study of fed-batch application of wastewater and sewage sludge treatment. Thus, the fed batch fermentation problems in winery wastewater treatment and biogas generation from sewage sludge are investigated and reformulated for optimization

Comparison of optimisation algorithms for centralised anaerobic co-digestion in a real river basin case study in Catalonia

Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its complexity increases when the wastes are from different types of generators. In this case, a key point to achieve good performance is optimisation methods. Currently, many tools have been developed to optimise a single AnD plant. However, the study of a network of AnD plants and multiple waste generators, all in different locations, remains unexplored. This novel approach requires the use of optimisation methodologies with the capacity to deal with a highly complex combinatorial problem. This paper proposes and compares the use of three evolutionary algorithms: ant colony optimisation (ACO), genetic algorithm (GA) and particle swarm optimisation (PSO), which are especially suited for this type of application. The algorithms successfully solve the problem, using an objective function that includes terms related to quality and logistics. Their application to a real case study in Catalonia (Spain) shows their usefulness (ACO and GA to achieve maximum biogas production and PSO for safer operation conditions) for AnD facilities.Peer ReviewedPostprint (published version

Cuckoo Search Approach for Parameter Identification of an Activated Sludge Process

A parameter identification problem for a hybrid model is presented. The latter describes the operation of an activated sludge process used for waste water treatment. Parameter identification problem can be considered as an optimization one by minimizing the error between simulation and experimental data. One of the new and promising metaheuristic methods for solving similar mathematical problem is Cuckoo Search Algorithm. It is inspired by the parasitic brood behavior of cuckoo species. To confirm the effectiveness and the efficiency of the proposed algorithm, simulation results will be compared with other algorithms, firstly, with a classical method which is the Nelder-Mead algorithm and, secondly, with intelligent methods such as Genetic Algorithm and Particle Swarm Optimization approaches

Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques

Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling

Model parameter uncertainty estimation based on Bayesian inference for activated sludge model under aerobic conditions: a comparison with a linear theory method

The purpose of the study is to apply Bayesian inference in order to estimate the uncertainty in model parameters and predictions for environmental models. The analysis was based on a global optimization routine that finds good initial values for an adaptive Markov chains Monte Carlo (MCMC) algorithm that finally computes the posterior parameter distribution. A revised activated sludge model was used in order to perform a comparison between Bayesian and linear theory methods. It was observed that the linear theory method systematically underestimates the confidence intervals of the estimated model parameters because the multivariate normality assumption is violated and practical unidentifiability for some parameters occurs.Postprint (published version

A Comprehensive Optimization Framework for Designing Sustainable Renewable Energy Production Systems

As the world has recognized the importance of diversifying its energy resource portfolio away from fossil resources and more towards renewable resources such as biomass, there arises a need for developing strategies which can design renewable sustainable value chains that can be scaled up efficiently and provide tangible net environmental benefits from energy utilization. The objective of this research is to develop and implement a novel decision-making framework for the optimal design of renewable energy systems. The proposed optimization framework is based on a distributed, systematic approach which is composed of different layers including systems-based strategic optimization, detailed mechanistic modeling and operational level optimization. In the strategic optimization the model is represented by equations which describe physical flows of materials across the system nodes and financial flows that result from the system design and material movements. Market uncertainty is also incorporated into the model through stochastic programming. The output of the model includes optimal design of production capacity of the plant for the planning horizon by maximizing the net present value (NPV). The second stage consists of three main steps including simulation of the process in the simulation software, identification of critical sources of uncertainties through global sensitivity analysis, and employing stochastic optimization methodologies to optimize the operating condition of the plant under uncertainty. To exemplify the efficacy of the proposed framework a hypothetical lignocellulosic biorefinery based on sugar conversion platform that converts biomass to value-added biofuels and biobased chemicals is utilized as a case study. Furthermore, alternative technology options and possible process integrations in each section of the plant are analysed by exploiting the advantages of process simulation and the novel hybrid optimization framework. In conjunction with the simulation and optimization studies, the proposed framework develops quantitative metrics to associate economic values with technical barriers. The outcome of this work is a new distributed decision support framework which is intended to help economic development agencies, as well as policy makers in the renewable energy enterprises

Response Surface Methodology and Genetic Algorithms Applied to Model and Optimize the Dyeing of Cotton Process with the Reactive Black 5 Dyestuff

This work aimed to combine response surface methodology and genetic algorithms to model and optimize the dyeing process to show the influences of each component in the dyeing of cotton knit to optimize its dyeing conditions. A 26 design of central composite and rotational (DCCR) was used as support to execute seventy-eight dyeings with Reactive Black 5 dyestuff (RB5) on 100% knitted cotton substrate. The impacts of various dyeing process parameters were also investigated. The concentrations of [RB5] (percent), [NaCl] (g/L), [Na2CO3] (g/L), and [NaOH] (mL/L), as well as processing time (min) and temperature (°C), were employed. The K S-1 coefficient and the costs of each experiment were calculated as a result. The objective function was derived from the fitting of the experimental points using the least-squares method and analysis of variance (ANOVA). The findings revealed that both techniques can be efficiently applied to model and optimize the cotton dyeing, with the goal of lowering the cost and environmental impact

Integrated Active Control Strategies and Licensing Approaches for Urban Wastewater Systems

The wastewater sector in the UK and other EU member states are facing stringent regulatory standards. The environmental water quality standards such as the EU-WFD, on the one hand, require a higher level of wastewater treatment which can result in increased GHG emissions and operational cost through higher energy use, chemical consumption, and capital investment. On the other hand, the Carbon Reduction Commitment Energy Efficiency scheme requires the water industries to reduce their GHG emission significantly. The research assesses the advantage of integrated active control of existing WWTPs, their optimisation and dynamic licensing approach to tackle this challenge while maintaining the quality of the receiving river. The dynamic licensing approach focuses on the design of control strategies based on the receiving river’s assimilative capacity. A simulation approach is used to test control strategies and their optimisation, interventions, and dynamic licensing approaches. The study developed an integrated UWWS model that fully integrate WWTP, sewer network, and receiving river, which enables the assessment of the advantage of integrated control strategies and dynamic licensing approach. The hybrid modelling approach uses mechanistic, conceptual and data-driven models in order to reduce computational cost while maintaining the model accuracy. Initially, the WWTP model was set up using average values of model parameters from the literature. However, this did not give a model with good accuracy. Hence, through, a careful design and identification of key parameters, a data campaign was designed to characterise influent wastewater, flow pattern, and biological processes of a real-world case study. The model accuracy was further improved using auto-calibration processes using a sensitivity analysis, identifying influential parameters to which the final effluent and oxidation ditch quality indicators are sensitive to. The sensitivity and auto-calibration were done using statistical measures that compare simulated and measured data points. Nash-Sutcliff coefficient (NSE) and root-mean-square-error (RMSE) measures show consistency in the sensitivity analysis, but correlation coefficient R2 showed a slight difference as it focusses on pattern similarity than values closeness. The combined use of NSE and RMSE gave the best result in model accuracy using fewer generation in the multi-objective optimisation using NSGA-II. Further local sensitivity analysis is used to identify the effect of varying control handles on GHG emissions (as equivalent CO2 emission), operational cost and effluent quality. The GHG emissions both from direct and indirect sources are considered in this study. The indirect GHG emissions consider the major GHG emissions (CO2, N2O, and CH4) associated with the use of electricity, sludge transport, and offsite degradation of sludge and final effluent. Similarly, the direct GHG emissions consider the emission of these major gases from different biological processes within the WWTP such as substrate utilisation, denitrification and biomass decay. This knowledge helps in the development of control strategies by indicating influential control handles and aids the selection of control strategies for optimisation purposes. It is found that multi-objective optimisation can reduce GHG emissions, operational cost while operating under the effluent quality standards. Multi-objective optimisation of control loops coupled with integrated active control of oxygen using final effluent ammonia concentration showed the highest reduction in GHG emissions and reduction in operational cost without violating the current effluent quality standard. Through dynamic licensing approach, the oxygen level in the oxidation ditch is controlled based on the assimilative capacity of the receiving river, which reduces the operational cost and effluent quality index without increased GHG emissions. However, to benefit from the dynamic licensing approach, a trade-off needs to be considered further between final effluent NO3 concentration and reduction in oxygen level in the oxidation ditch to reduce biomass decay which is responsible for higher GHG emission in this scenario

Parametric Optimization for the Maximization of Hydrogen Production by Enterobacter Cloacae

The decrease of fossil fuel energy induces the development of sustaining renewable energy. One of the potential energy to be further developed is hydrogen energy. Most of the hydrogen resources currently come from fossil fuel energy. Besides, some biological processes also can produce hydrogen such as dark fermentation which is being focused on in this project. Enterobacter cloacae are used as the bacteria to be fermented in the nutrient broth. Since this process has yet to achieve economic sustainability, this project focuses on the maximization of the production of hydrogen gas by optimizing the parameters influencing the hydrogen production. The decision variables (process parameters) are the initial glucose concentration, Inoculum age and also the initial pH of the nutrient broth. By using data from the previous research, the parameters are optimized by using three numerical methods, simulated annealing, pattern search and Genetic algorithm. A comparison between these three algorithms used is done to compare the optimization results and discuss their advantages and disadvantages