31 results found
Ali H, Solsvik J, Wagner JL, et al., 2019, CFD and kinetic-based modeling to optimize the sparger design of a large-scale photobioreactor for scaling up of biofuel production, BIOTECHNOLOGY AND BIOENGINEERING, Vol: 116, Pages: 2200-2211, ISSN: 0006-3592
Del Rio-Chanona EA, Ahmed NR, Wagner J, et al., 2019, Comparison of physics-based and data-driven modelling techniques for dynamic optimisation of fed-batch bioprocesses, BIOTECHNOLOGY AND BIOENGINEERING, Vol: 116, Pages: 2971-2982, ISSN: 0006-3592
Zhang D, Del Rio-Chanona EA, Petsagkourakis P, et al., 2019, Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization, BIOTECHNOLOGY AND BIOENGINEERING, Vol: 116, Pages: 2919-2930, ISSN: 0006-3592
del Rio Chanona EA, Wagner JL, Ali H, et al., 2019, Deep learning-based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design, AIChE Journal, Vol: 65, Pages: 915-923, ISSN: 0001-1541
Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae‐derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modeling. However, this approach presents computational intractability and numerical instabilities when simulating large‐scale systems, causing time‐intensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative data‐driven surrogate modeling framework, which considerably reduces computing time from months to days by exploiting state‐of‐the‐art deep learning technology. The framework built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Through verification, this framework was demonstrated to have comparable accuracy to the physical model. Moreover, multi‐objective optimization was incorporated to generate a Pareto‐frontier for decision‐making, advancing its applications in complex biosystems modeling and optimization.
Del Rio-Chanona EA, Cong X, Bradford E, et al., 2019, Review of advanced physical and data-driven models for dynamic bioprocess simulation: Case study of algae-bacteria consortium wastewater treatment, BIOTECHNOLOGY AND BIOENGINEERING, Vol: 116, Pages: 342-353, ISSN: 0006-3592
Petsagkourakis P, Sandoval IO, Bradford E, et al., 2019, Reinforcement Learning for Batch-to-Batch Bioprocess Optimisation, Computer Aided Chemical Engineering, Pages: 919-924
Bioprocesses have received great attention from the scientific community as an alternative to fossil-based products by microorganisms-synthesised counterparts. However, bioprocesses are generally operated at unsteady-state conditions and are stochastic from a macro-scale perspective, making their optimisation a challenging task. Furthermore, as biological systems are highly complex, plant-model mismatch is usually present. To address the aforementioned challenges, in this work, we propose a reinforcement learning based online optimisation strategy. We first use reinforcement learning to learn an optimal policy given a preliminary process model. This means that we compute diverse trajectories and feed them into a recurrent neural network, resulting in a policy network which takes the states as input and gives the next optimal control action as output. Through this procedure, we are able to capture the previously believed behaviour of the biosystem. Subsequently, we adopted this network as an initial policy for the “real” system (the plant) and apply a batch-to-batch reinforcement learning strategy to update the network's accuracy. This is computed by using a more complex process model (representing the real plant) embedded with adequate stochasticity to account for the perturbations in a real dynamic bioprocess. We demonstrate the effectiveness and advantages of the proposed approach in a case study by computing the optimal policy in a realistic number of batch runs.
del Rio-Chanona EA, Zhang D, 2018, A Bilevel Programming Approach to Optimize C-phycocyanin Bio-production under Uncertainty, 10th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), Publisher: ELSEVIER SCIENCE BV, Pages: 209-214, ISSN: 2405-8963
Bradford E, Schweidtmann AM, Zhang D, et al., 2018, Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes, COMPUTERS & CHEMICAL ENGINEERING, Vol: 118, Pages: 143-158, ISSN: 0098-1354
Harun I, Del Rio-Chanona EA, Wagner JL, et al., 2018, Photocatalytic Production of Bisabolene from Green Microalgae Mutant: Process Analysis and Kinetic Modeling, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 57, Pages: 10336-10344, ISSN: 0888-5885
Zhang D, del Rio-Chanona EA, Shah N, 2018, Life cycle assessments for biomass derived sustainable biopolymer & energy co-generation, Sustainable Production and Consumption, Vol: 15, Pages: 109-118, ISSN: 2352-5509
Sustainable polymers derived from biomass have been considered as promising candidates to reduce the dependency on fossil based polymers. In this study, a conceptual process design was conducted for citrus waste derived biopolymer production with energy co-generation, and its eco-friendliness was evaluated through life cycle assessment by comparison against a petroleum derived polymer production process. Based on the current research, two original conclusions were proposed. The first one is that energy-efficient separation techniques are of critical importance for the design of eco-friendly chemical processes. Only focusing on the use of sustainable feedstocks with high conversion reactions cannot guarantee an environmentally-friendly final product. The second one is that biomass should be considered not only as a raw material, but more importantly, as an energy source for the sustainable synthesis of biochemicals. In other words, a sustainable process should be designed such that a portion of biomass is used to provide clean energy for process operation, with the rest converted for product generation.
Palamae S, Choorit W, Dechatiwongse P, et al., 2018, Production of renewable biohydrogen by Rhodobacter sphaeroides S10: A comparison of photobioreactors, JOURNAL OF CLEANER PRODUCTION, Vol: 181, Pages: 318-328, ISSN: 0959-6526
Zhang D, del Rio-Chanona EA, Wagner JL, et al., 2018, Life cycle assessments of bio-based sustainable polylimonene carbonate production processes, Sustainable Production and Consumption, Vol: 14, Pages: 152-160, ISSN: 2352-5509
Biomass is a promising feedstock for the production of sustainable biopolymers, which could offer a significant reduction of the adverse environmental impacts associated with conventional petroleum-based polymers. To further evaluate their potential, this study investigated the environmental impacts associated with the production of the newly proposed biopolymer polylimonene carbonate. Different feedstocks (citrus waste and microalgae) were selected and a conceptual process design from limonene oxidation to polymer synthesis was completed. Using life cycle assessment, the potential for energy integration and the contributions of individual process sections on the overall process environmental impacts were thoroughly analysed. The results showed, that sustainable polylimonene carbonate synthesis was limited by the use of tert-butyl hydroperoxide as the limonene oxidation agent and consequently, a more environmentally-friendly and energy-efficient limonene oxidation method should be developed. Based on the economic analysis, the polymer cost was estimated to range from $1.36 to $1.51 kg −1 , comparable to the costs of petrol-based polystyrene ($1.2 to $1.6 kg −1 ). Moreover, this study found that both feedstock selection and the biowaste treatment method have significant effects on the process environmental impacts, and a carbon negative process was achieved when applying the waste biomass for electricity generation. Therefore, it was concluded that future process designs should combine polymer production with the co-generation of energy from waste biomass.
Jing K, Tang Y, Yao C, et al., 2018, Overproduction of L-tryptophan via simultaneous feed of glucose and anthranilic acid from recombinant Escherichia coli W3110: Kinetic modeling and process scale-up, BIOTECHNOLOGY AND BIOENGINEERING, Vol: 115, Pages: 371-381, ISSN: 0006-3592
del Rio-Chanona EA, Liu J, Wagner JL, et al., 2018, Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production, Biotechnology and Bioengineering, Vol: 115, Pages: 359-370, ISSN: 1097-0290
Biodiesel produced from microalgae has been extensively studied due to its potentially outstanding advantages over traditional transportation fuels. In order to facilitate its industrialization and improve the process profitability, it is vital to construct highly accurate models capable of predicting the complex behavior of the investigated biosystem for process optimization and control, which forms the current research goal. Three original contributions are described in this paper. Firstly, a dynamic model is constructed to simulate the complicated effect of light intensity, nutrient supply and light attenuation on both biomass growth and biolipid production. Secondly, chlorophyll fluorescence, an instantly measurable variable and indicator of photosynthetic activity, is embedded into the model to monitor and update model accuracy especially for the purpose of future process optimal control, and its correlation between intracellular nitrogen content is quantified, which to the best of our knowledge has never been addressed so far. Thirdly, a thorough experimental verification is conducted under different scenarios including both continuous illumination and light/dark cycle conditions to testify the model predictive capability particularly for long-term operation, and it is concluded that the current model is characterized by a high level of predictive capability. Based on the model, the optimal light intensity for algal biomass growth and lipid synthesis is estimated. This work, therefore, paves the way to forward future process design and real-time optimization.
Zhang D, del Rio-Chanona EA, Shah N, 2018, Life cycle assessment of bio-based sustainable polylimonene carbonate production processes, Computer Aided Chemical Engineering, Pages: 1693-1698
Biomass derived polymers are considered as promising candidates to replace petroleum based polymers due to their potential environmental friendliness. To facilitate their application, in this study, a newly proposed biopolymer, polylimonene carbonate, was chosen as the representative to investigate the environmental impacts of the biopolymer production process. Different feedstocks (citrus waste and microalgae) were selected and a comprehensive process design from limonene oxidation to polymer synthesis was completed. Through life cycle assessment, effects of biomass treatment methods, energy integration, and use of solvents on the process environmental impacts were thoroughly discussed. It was found that for sustainable polylimonene carbonate synthesis, a more environmentally-friendly and energy-efficient limonene oxidation method should be developed. Based on the economic analysis, the polymer's cost was estimated to be around 1.36 to 1.51 $/kg, indicating its great potential as a substitute for petrol-based polystyrene. Moreover, this study found that both feedstock selection and biowaste treatment method significantly affect the process environmental impacts, and a carbon negative biopolymer can be achieved when the remaining waste is used for energy generation. Therefore, a new concept that considers CO2 as an efficient solar energy carrier for future sustainable process design is proposed in this study.
Zhang D, del Rio-Chanona EA, 2018, A Bilevel Programming Approach for the Dynamic Optimization of Cyanobacterial C-phycocyanin Production under Uncertainty, Computer Aided Chemical Engineering, Pages: 535-540
C-phycocyanin is a high-value bioproduct synthesized by cyanobacterium Arthrospira platensis with a significant global market demand given its applications in the pharmaceutical, food and colorant industries. Unfortunately, its biosynthesis is currently characterized by low productivity and large uncertainty during the production process. High variability and unreliable expectations on product yields substantially hinder the industrialization of microorganism derived biochemicals as they present a risk to the profitability and safety of the underlying systems. Therefore, in this work, we propose a robust optimization approach to determine the lower and upper product yield expectations for the sustainable production of C-phycocyanin. Kinetic modeling is adopted in this study as a tool for fast prototyping, prediction and optimization of chemical and biochemical processes. On the upside, parameters in bioprocess kinetic models are used as a simplification of the complex metabolic networks to enable the simulation, design and control of the process. On the downside, this conglomeration of parameters may result in significant model uncertainty. To address this challenge, we formulate a bilevel max-min optimization problem to obtain the worst-case scenario of our system given the uncertainty on the model parameters. By constructing parameter confidence ellipsoids, we determined the feasible region along which the parameters can minimize the system's performance, while nutrient and light controls are used to maximize the biorenewable production. The inner minimization problem is embedded by means of the optimality conditions into the upper maximization problem and hence both are solved simultaneously. Through this approach, we determined pessimistic and optimistic scenarios for the bioproduction of C-phycocyanin and hence compute reliable expectations on the yield and profit of the process.
del Rio-Chanona EA, Zhang D, Shah N, 2017, Sustainable biopolymer synthesis via superstructure and multiobjective optimization, AIChE Journal, Vol: 64, Pages: 91-103, ISSN: 0001-1541
Sustainable polymers derived from biomass have great potential to replace petrochemical based polymers and fulfill the ever-increasing market demand. To facilitate their industrialization, in this research, a comprehensive superstructure reaction network comprising a large number of reaction pathways from biomass to both commercialized and newly proposed polymers is constructed. To consider economic performance and environmental impact simultaneously, both process profit and green chemistry metrics are embedded into the multiobjective optimization framework, and MINLP is used to enable the effective selection of promising biopolymer candidates. Through this proposed approach, this study identifies the best biopolymer candidates and their most profitable and environmentally friendly synthesis routes under different scenarios. Moreover, the stability of optimization results regarding the price of raw materials and polymers and the effect of process scale on the investment cost are discussed in detail. These results, therefore, pave the way for future research on the production of sustainable biopolymers.
del Rio-Chanona EA, Fiorelli F, Zhang D, et al., 2017, An efficient model construction strategy to simulate microalgal lutein photo-production dynamic process, Biotechnology and Bioengineering, Vol: 114, Pages: 2518-2527, ISSN: 1097-0290
Lutein is a high-value bioproduct synthesized by microalga Desmodesmus sp. It has great potential for the food, cosmetics, and pharmaceutical industries. However, in order to enhance its productivity and to fulfil its ever-increasing global market demand, it is vital to construct accurate models capable of simulating the entire behavior of the complicated dynamics of the underlying biosystem. To this aim, in this study two highly robust artificial neural networks (ANNs) are designed for the first time. Contrary to conventional ANNs, these networks model the rate of change of the dynamic system, which makes them highly relevant in practice. Different strategies are incorporated into the current research to guarantee the accuracy of the constructed models, which include determining the optimal network structure through a hyper-parameter selection framework, generating significant amounts of artificial data sets by embedding random noise of appropriate size, and rescaling model inputs through standardization. Based on experimental verification, the high accuracy and great predictive power of the current models for long-term dynamic bioprocess simulation in both real-time and offline frameworks are thoroughly demonstrated. This research, therefore, paves the way to significantly facilitate the future investigation of lutein bioproduction process control and optimization. In addition, the model construction strategy developed in this research has great potential to be directly applied to other bioprocesses.
del Rio-Chanona EA, Ahmed NR, Zhang D, et al., 2017, Kinetic modeling and process analysis for Desmodesmus sp. lutein photo-production, AIChE Journal, Vol: 63, Pages: 2546-2554, ISSN: 0001-1541
Zhang D, del Rio-Chanona EA, Shah N, 2017, Screening synthesis pathways for biomass-derived sustainable polymer production, ACS Sustainable Chemistry and Engineering, Vol: 5, Pages: 4388-4398, ISSN: 2168-0485
Sustainable polymers derived from biomass have been extensively investigated to replace petroleum-based polymers and fulfill the ever-increasing market demand. Because of the diversity of biomass and polymer categories, there exists a large number of synthesis routes from biomass to polymers. However, their productive and economic potentials have never been evaluated. Therefore, in this study, a comprehensive reaction network covering the synthesis of 20 polymers, including both newly proposed biopolymers and traditional polymers, is constructed to resolve this challenge for the first time. Through the network, over 100 synthesis pathways are screened to identify the most promising biopolymers. Three original contributions are concluded. First, from a carbon point of view, polyethylene and 1,4-cyclohexadiene-based polymers are found to be the best petroleum-based polymer and newly proposed biopolymers that can be produced from biomass, respectively, because of their highest carbon recovery efficiency of ∼70%. Second, an external hydrogen supply is vital to guarantee the high yield of biopolymer, because, without enough hydrogen, biopolymer productivity can be reduced by half. Third, through sensitivity analysis, the current biopolymer ranking is verified to be stable, subject to a moderate change of reaction selectivities and hydrogen supply. Therefore, this study provides a clear direction for future biopolymer research.
del Rio-Chanona EA, Zhang D, Vassiliadis VS, 2016, Model-based real-time optimisation of a fed-batch cyanobacterial hydrogen production process using economic model predictive control strategy, CHEMICAL ENGINEERING SCIENCE, Vol: 142, Pages: 289-298, ISSN: 0009-2509
del Rio-Chanona EA, Manirafasha E, Zhang D, et al., 2015, Dynamic modeling and optimization of cyanobacterial C-phycocyanin production process by artificial neural network, Algal Research, Vol: 13, Pages: 7-15, ISSN: 2211-9264
Artificial neural networks have been widely applied in bioprocess simulation and control due to their advantageous properties. However, their feasibility in long-term photo-production process modeling and prediction as well as their efficiency on process optimization have not been well studied so far. In the current study, an artificial neural network was constructed to simulate a 15-day fed-batch process for cyanobacterial C-phycocyanin production, which to the best of our knowledge has never been conducted. To guarantee the accuracy of artificial neural network, two strategies were implemented. The first strategy is to generate artificial data sets by adding random noise to the original data set, and the second is to choose the change of state variables as training data output. In addition, the first strategy showed the distinctive advantage of reducing the experimental effort in generating training data. By comparing with current experimental results, it is concluded that both strategies give the network great modeling and predictive power to estimate the entire fed-batch process performance, even when few original experimental data are supplied. Furthermore, by optimizing the operating conditions of a 12-day fed-batch process, a significant increase of 85.6% on C-phycocyanin production was achieved compared to previous work, which suggests the high efficiency of artificial neural network on process optimization.
Zhang D, Wan M, del Rio-Chanona EA, et al., 2015, Dynamic modelling of Haematococcus pluvialis photoinduction for astaxanthin production in both attached and suspended photobioreactors, Algal Research, Vol: 13, Pages: 69-78, ISSN: 2211-9264
Haematococcus pluvialis is a green algae with the great potential to generate natural astaxanthin. In the current study, dynamic models are proposed to simulate effects of light intensity, light attenuation, temperature and nitrogen quota on cell growth and astaxanthin production in both suspended and attached photobioreactors, which to the best of our knowledge has not been addressed before. Based on the current models, optimal temperatures for algal growth and astaxanthin accumulation are identified. Cell absorption is found to be the primary factor causing light attenuation in the suspended reactor. In this reactor, astaxanthin accumulation is limited by the low local light intensity due to light attenuation during the initial operation period, but almost independent from that once it is close to the maximum value. Compared to the suspended reactor, light attenuation in the attached reactor is much reduced and biomass growth is remarkably enhanced, which suggests that the attached reactor is a better choice if the process aims for biomass cultivation. However, the well-mixed culture in the suspended reactor can push most cells toward astaxanthin production; while the attached reactor has the potential to prevent the accumulation of astaxanthin in the bottom algae. Therefore, the suspended photobioreactor should be selected if the process target is astaxanthin production.
del Rio-Chanona EA, Zhang D, Xie Y, et al., 2015, Dynamic Simulation and Optimization for Arthrospira platensis Growth and C-Phycocyanin Production, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 54, Pages: 10606-10614, ISSN: 0888-5885
Zhang D, Chanona EAD-R, Vassiliadis VS, et al., 2015, Analysis of green algal growth via dynamic model simulation and process optimization, BIOTECHNOLOGY AND BIOENGINEERING, Vol: 112, Pages: 2025-2039, ISSN: 0006-3592
Zhang D, Xiao N, Mahbubani KT, et al., 2015, Bioprocess modelling of biohydrogen production by Rhodopseudomonas palustris: Model development and effects of operating conditions on hydrogen yield and glycerol conversion efficiency, CHEMICAL ENGINEERING SCIENCE, Vol: 130, Pages: 68-78, ISSN: 0009-2509
del Rio-Chanona EA, Dechatiwongse P, Zhang D, et al., 2015, Optimal Operation Strategy for Biohydrogen Production, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 54, Pages: 6334-6343, ISSN: 0888-5885
Zhang D, Dechatiwongse P, del Rio-Chanona EA, et al., 2015, Dynamic modelling of high biomass density cultivation and biohydrogen production in different scales of flat plate photobioreactors, Biotechnology and Bioengineering, Vol: 112, Pages: 2429-2438, ISSN: 1097-0290
This paper investigates the scaling-up of cyanobacterial biomass cultivation and biohydrogen production from laboratory to industrial scale. Two main aspects are investigated and presented, which to the best of our knowledge have never been addressed, namely the construction of an accurate dynamic model to simulate cyanobacterial photo-heterotrophic growth and biohydrogen production and the prediction of the maximum biomass and hydrogen production in different scales of photobioreactors. To achieve the current goals, experimental data obtained from a laboratory experimental setup are fitted by a dynamic model. Based on the current model, two key original findings are made in this work. First, it is found that selecting low-chlorophyll mutants is an efficient way to increase both biomass concentration and hydrogen production particularly in a large scale photobioreactor. Second, the current work proposes that the width of industrial scale photobioreactors should not exceed 0.20 m for biomass cultivation and 0.05 m for biohydrogen production, as severe light attenuation can be induced in the reactor beyond this threshold.
Zhang D, Dechatiwongse P, Del-Rio-Chanona EA, et al., 2015, Analysis of the cyanobacterial hydrogen photoproduction process via model identification and process simulation, CHEMICAL ENGINEERING SCIENCE, Vol: 128, Pages: 130-146, ISSN: 0009-2509
Zhang D, Dechatiwongse P, del Rio-Chanona EA, et al., 2015, Modelling of light and temperature influences on cyanobacterial growth and biohydrogen production, Algal Research, Vol: 9, Pages: 263-274, ISSN: 2211-9264
Dynamic simulation is a valuable tool to assist the scale-up and transition of biofuel production from laboratory scale to potential industrial implementation. In the present study two dynamic models are constructed, based on the Aiba equation, the improved Lambert–Beer's law and the Arrhenius equation. The aims are to simulate the effects of incident light intensity, light attenuation and temperature upon the photo-autotrophic growth and the hydrogen production of the nitrogen-fixing cyanobacterium Cyanothece sp. ATCC 51142. The results are based on experimental data derived from an experimental setup using two different geometries of laboratory scale photobioreactors: tubular and flat-plate. All of the model parameters are determined by an advanced parameter estimation methodology and subsequently verified by sensitivity analysis. The optimal temperature and light intensity facilitating biohydrogen production in the absence of light attenuation have been determined computationally to be 34 °C and 247 μmol m− 2 s− 1, respectively, whereas for cyanobacterial biomass production they are 37 °C and 261 μmol m− 2 s− 1, respectively. Biomass concentration higher than 0.8 g L− 1 is also demonstrated to significantly enhance the light attenuation effect, which in turn inducing photolimitation phenomena. At a higher biomass concentration (3.5 g L− 1), cyanobacteria are unable to activate photosynthesis to maintain their lives in a photo-autotrophic growth culture, and biohydrogen production is significantly inhibited due to the severe light attenuation.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.