Automated structure detection for distributed process optimization

The design and control of large-scale engineering systems, consisting of a number of interacting subsystems, is a heavily researched topic with relevance both for industry and academia. This paper presents two methodologies for optimal model-based decomposition, where an optimization problem is decomposed into several smaller sub-problems and subsequently solved by augmented Lagrangian decomposition methods. Large-scale and highly nonlinear problems commonly arise in process optimization, and could greatly benefit from these approaches, as they reduce the storage requirements and computational costs for global optimization. The strategy presented translates the problem into a constraint graph. The first approach uses a heuristic community detection algorithm to identify highly connected clusters in the optimization problem graph representation. The second approach uses a multilevel graph bisection algorithm to find the optimal partition, given a desired number of sub-problems. The partitioned graphs are translated back into decomposed sets of sub-problems with a minimal number of coupling constraints. Results show both of these methods can be used as efficient frameworks to decompose optimization problems in linear time, in comparison to traditional methods which require polynomial time.Author E. A. del Rio-Chanona would like to acknowledge CONACyT scholarship No. 522530 for funding this project. Author F. Fiorelli gratefully acknowledges the support from his family. The authors would also 27 like to thank Dr Bart Hallmark, University of Cambridge, for suggesting to employ as a demonstration the chemical system in Example 7.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.compchemeng.2016.03.01

Application of gaussian processes to online approximation of compressor maps for load-sharing in a compressor station

Devising optimal operating strategies for a compressor station relies on the knowledge of compressor characteristics. As the compressor characteristics change with time and use, it is necessary to provide accurate models of the characteristics that can be used in optimization of the operating strategy. This paper proposes a new algorithm for online learning of the characteristics of the compressors using Gaussian Processes. The performance of the new approximation is shown in a case study with three compressors. The case study shows that Gaussian Processes accurately capture the characteristics of compressors even if no knowledge about the characteristics is initially available. The results show that the flexible nature of Gaussian Processes allows them to adapt to the data online making them amenable for use in real-time optimization problems

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

Model‐based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low‐quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics‐based and data‐driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high‐quality data by correcting raw process measurements via a physics‐based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data‐driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re‐fitting the simple kinetic model (soft sensor) using the data‐driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed‐batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open‐loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application

Dynamic Simulation and Optimization for Arthrospira platensis Growth and C-Phycocyanin Production

This is the accepted manuscript. The final version is available at http://pubs.acs.org/doi/abs/10.1021/acs.iecr.5b03102.C-phycocyanin is a high-value bioproduct synthesized from cyanobacterium Arthrospira platensis. To facilitate its application, advanced dynamic models were built to simulate the complex effects of light intensity, light attenuation and nitrate concentration on cell growth and pigment production in the current research. By comparing these models against the experimental results, their accuracy was verified in both batch and fed-batch processes. Three key findings are presented in this work. First, a noticeable difference between the optimal light intensity for cell growth (282 μmol m-2 s-1) and phycocyanin synthesis (137 μmol m-2 s-1) is identified. Second, light attenuation is demonstrated to be the primary factor causing the decrease of intracellular phycocyanin content instead of nitrate concentration in the fed-batch process, while it has no significant effect on total phycocyanin production. Finally, although high nitrate concentration can enhance cell growth, it is demonstrated to suppress intracellular phycocyanin accumulation in a long-term operation.Author E. A. del Rio-Chanona is funded by CONACyT scholarship No. 522530 and the Secretariat of Public Education and the Mexican government. This work was also supported by the National High Technology Research and Development Program 863, China (No. 2014AA021701) and the National Marine Commonwealth Research Program, China (No. 201205020-2)

Model-based real-time optimisation of a fed-batch cyanobacterial hydrogen production process using economic model predictive control strategy

Hydrogen produced by microorganisms has been considered as a potential solution for sustainable hydrogen production for the future. In the current study, an advanced real-time optimisation methodology is developed to maximise the productivity of a 21-day fed-batch cyanobacterial hydrogen production process, which to the best of our knowledge has not been addressed before. This methodology consists of an economic model predictive control formulation used to predict the future experimental performance and identify the future optimal control actions, and a finite-data window least-squares procedure to re-estimate model parameter values of the on-going process and ensure the high accuracy of the dynamic model. To explore the efficiency of the current optimisation methodology, effects of its essential factors including control position, prediction horizon length, estimation window length, model synchronising frequency, terminal region and terminal cost on hydrogen production have been analysed. Finally, by implementing the proposed optimisation strategy into the current computational fed-batch experiment, a significant increase of 28.7% on hydrogen productivity is achieved compared to the previous study.E. A. del Rio-Chanona is funded by CONACyT scholarship No. 522530 and from the Secretariat of Public Education and the Mexican government.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.ces.2015.11.04

Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty

Dynamic real-time optimization (DRTO) is a challenging task due to the fact that optimal operating conditions must be computed in real time. The main bottleneck in the industrial application of DRTO is the presence of uncertainty. Many stochastic systems present the following obstacles: 1) plant-model mismatch, 2) process disturbances, 3) risks in violation of process constraints. To accommodate these difficulties, we present a constrained reinforcement learning (RL) based approach. RL naturally handles the process uncertainty by computing an optimal feedback policy. However, no state constraints can be introduced intuitively. To address this problem, we present a chance-constrained RL methodology. We use chance constraints to guarantee the probabilistic satisfaction of process constraints, which is accomplished by introducing backoffs, such that the optimal policy and backoffs are computed simultaneously. Backoffs are adjusted using the empirical cumulative distribution function to guarantee the satisfaction of a joint chance constraint. The advantage and performance of this strategy are illustrated through a stochastic dynamic bioprocess optimization problem, to produce sustainable high-value bioproducts

Analysis of the cyanobacterial hydrogen photoproduction process via model identification and process simulation

Cyanothece sp. ATCC 51142 is considered a microorganism with the potential to generate sustainable hydrogen in the future. However, few kinetic models are capable of simulating different phases of Cyanothece sp. ATCC 51142 from growth to hydrogen production. In the present study four models are constructed to simulate Cyanothece sp. batch photoproduction process. A dynamic optimisation method is used to determine parameters in the models. It is found that although the piecewise models fit experimental data better, large deviation can be induced when they are used to simulate a process whose operating conditions are different from the current experiments. The modified models are eventually selected in the present study to simulate a two-stage continuous photoproduction process. The current simulation results show that a plug flow reactor (PFR) shows worse performance compared to a continuous stirred-tank reactor (CSTR) in the current operating conditions since it lowers the total hydrogen production. The finding is that nitrate and oxygen concentration change along the direction of culture movement in PFR, and hydrogen is only generated in the zone where both of them are low. The reactor area thereby is not well utilised. Additionally, as hydrogen production rate is primarily influenced by biomass concentration, which increases initially and decreases eventually along the direction of culture movement, the overall hydrogen production rate in a PFR may be lower than that in a CSTR. Finally, in this study fed-batch photoproduction processes are proposed containing only one photobioreactor based on the current simulation.Solar Hydrogen Project was funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Project reference EP/F00270X/1. The author E.A. del Rio-Chanona funding by CONACyT Scholarship No. 522530 scholarship from the Secretariat of Public Education and the Mexican government.This is the final published version. It first appeared at http://www.sciencedirect.com/science/article/pii/S0009250915000883#

Construction of global optimization constrained NLP test cases from unconstrained problems

This paper presents a novel construction technique for constrained nonconvex Nonlinear Programming Problem (NLP) test cases, derived from the evaluation tree structure of standardized bound constrained problems for which the global solution is known. It is demonstrated in a step-by-step procedure how first an equality constrained problem can be derived from an unconstrained one, with bounds imposed on all variables, using the Directed Acyclic Graph (DAG) of the unconstrained objective function and the use of interval arithmetic to derive bounds for the new variables introduced. An advantage of the proposed methodology is that several standard unconstrained global optimization test cases can be constructed for varying number of optimization variables, thus leading to adjustable size derived NLP’s. Further to this in a second step it is demonstrated how any subset of the equalities derived can be relaxed into inequalities giving an equivalent optimization problem. Finally, in a third step it is demonstrated how, by reducing the number of equality constraints derived, it is possible to obtain more complex expressions in the constraints and objective function. The methodology is highlighted throughout by motivating examples and a sample code in Mathematica TM is provided in the Appendix.This is the author accepted manuscript. The final version is available from Elsevier via https://doi.org/https://doi.org/10.1016/j.cherd.2016.03.01

Modelling of light and temperature influences on cyanobacterial growth and biohydrogen production

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.The author D. Zhang gratefully acknowledges the support from his family. The author P. Dechatiwongse is supported by a scholarship from the Royal Thai Government, Thailand, and his project, Solar Hydrogen Project, was funded by the UK Engineering and Physical Sciences Research Council (EPSRC), project reference EP/F00270X/1. Author E. A. del Rio-Chanona is funded by CONACyT scholarship No. 522530 from the Secretariat of Public Education and the Mexican government. The authors wish to thank Mr. Fabio Fiorelli for his invaluable advice and support during the preparation of this work.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.algal.2015.03.01

Bioprocess modelling of biohydrogen production by Rhodopseudomonas palustris: Model development and effects of operating conditions on hydrogen yield and glycerol conversion efficiency

This research explores the photofermentation of glycerol to hydrogen by Rhodopseudomonas palus- tris, with the objective to maximise hydrogen production. Two piecewise models are designed to simulate the entire growth phase of R. palustris; a challenge that few dynamic models can accomplish. The parameters in both models were fitted by the present batch experiments through the solution of the underlying optimal control problems by means of stable and accurate discretisation techniques. It was found that an initial glutamate to glycerol ratio of 0.25 was optimal, and was independent of the initial biomass concentration. The glycerol conversion efficiency was found to depend on initial biomass concentration and its computational peak is 64.4%. By optimising a 30-day industrially relevant batch process, the hydrogen productivity was improved to be 37.7 mL·g biomass-1·hr-1 and the glycerol conversion efficiency was maintained at 58%. The models can then be applied as the connection to transfer biohydrogen production from laboratory scale into industrial scale.Authors N. Xiao and Dr. K. T. Mahbubani are funded through the KACST-Cambridge Center for Advanced Material Manufacture, the author E. A. del Rio-Chanona is found by CONACyT scholarship No. 522530 from the Secretariat of Public Education and the Mexican government.This is the accepted manuscript. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S0009250915001815