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

Dynamic modelling of Haematococcus pluvialis photoinduction for astaxanthin production in both attached and suspended photobioreactors

Haematococcus pluvialis is a green algae with the great potential to generate natural astaxanthin. In the current study, dynamic models have been 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 temperature s 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 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.Author D. Zhang gratefully acknowledges the support from his family. Author M. Wan, W. Wang, J. Huang and Y. Li are funded by National High Technology Research and Development Program of China (2015AA020602), National Key Technologies R&D Program (2011BAD23B04), National Basic Research Program of China (973 Program: 2011CB200903 & 2011CB200904), China Postdoctoral Science Foundation (2014T70400), the Fundamental Research Funds for the Central Universities (222201414024). Author E. A. del Rio-Chanona is funded by CONACyT scholarship No. 522530 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.algal.2015.11.01

On the solution of differential-algebraic equations through gradient flow embedding

In this paper Gradient Flow methods are used to solve systems of differential-algebraic equations via a novel reformulation strategy, focusing on the solution of index-1 differential-algebraic equation systems. A reformulation is first effected on semi-explicit index-1 differential-algebraic equation systems, which casts them as pure ordinary differential equation systems subject to an embedded pointwise least-squares problem. This is then formulated as a gradient flow optimization problem. Rigorous proofs for this novel scheme are provided for asymptotic and epsilon convergence. The computational results validate the predictions of the effectiveness of the proposed approach, with efficient and accurate solutions obtained for the case studies considered. Beyond the theoretical and practical value for the solution of DAE systems as pure ODE ones, the methodology is expected to have an impact in similar cases where an ODE system is subjected to algebraic constraints, such as the Hamiltonian necessary conditions of optimality in optimal control problems.Author E. A. del Rio-Chanona would like to acknowledge CONACyT scholarship No. 522530 for funding this project

Occupational mobility and automation: A data-driven network model

The potential impact of automation on the labour market is a topic that has generated significant interest and concern amongst scholars, policymakers and the broader public. A number of studies have estimated occupation-specific risk profiles by examining how suitable associated skills and tasks are for automation. However, little work has sought to take a more holistic view on the process of labour reallocation and how employment prospects are impacted as displaced workers transition into new jobs. In this article, we develop a data-driven model to analyse how workers move through an empirically derived occupational mobility network in response to automation scenarios. At a macro level, our model reproduces the Beveridge curve, a key stylized fact in the labour market. At a micro level, our model provides occupation-specific estimates of changes in short and long-term unemployment corresponding to specific automation shocks. We find that the network structure plays an important role in determining unemployment levels, with occupations in particular areas of the network having few job transition opportunities. In an automation scenario where low wage occupations are more likely to be automated than high wage occupations, the network effects are also more likely to increase the long-term unemployment of low-wage occupations

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

Dynamic modeling is an important tool to gain better understanding of complex bioprocesses and to determine optimal operating conditions for process control. Currently, two modeling methodologies have been applied to biosystems: kinetic modeling, which necessitates deep mechanistic knowledge, and artificial neural networks (ANN), which in most cases cannot incorporate process uncertainty. The goal of this study is to introduce an alternative modeling strategy, namely Gaussian processes (GP), which incorporates uncertainty but does not require complicated kinetic information. To test the performance of this strategy, GPs were applied to model microalgae growth and lutein production based on existing experimental datasets and compared against the results of previous ANNs. Furthermore, a dynamic optimization under uncertainty is performed, avoiding over-optimistic optimization outside of the model’s validity. The results show that GPs possess comparable prediction capabilities to ANNs for long-term dynamic bioprocess modeling, while accounting for model uncertainty. This strongly suggests their potential applications in bioprocess systems engineering

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

Dynamic modeling is an important tool to gain better understanding of complex bioprocesses and to determine optimal operating conditions for process control. Currently, two modeling methodologies have been applied to biosystems: kinetic modeling, which necessitates deep mechanistic knowledge, and artificial neural networks (ANN), which in most cases cannot incorporate process uncertainty. The goal of this study is to introduce an alternative modeling strategy, namely Gaussian processes (GP), which incorporates uncertainty but does not require complicated kinetic information. To test the performance of this strategy, GPs were applied to model microalgae growth and lutein production based on existing experimental datasets and compared against the results of previous ANNs. Furthermore, a dynamic optimization under uncertainty is performed, avoiding over-optimistic optimization outside of the model’s validity. The results show that GPs possess comparable prediction capabilities to ANNs for long-term dynamic bioprocess modeling, while accounting for model uncertainty. This strongly suggests their potential applications in bioprocess systems engineering