Conditional Optimization of Laccase Production by Whiterot Fungi through Fermentation

Phanerochaete chrysosporium was selected as the production strain of laccase,and the effects of stirring speed,ventilation volume,culture temperature,inoculation amount and initial p H of medium on laccase production by liquid fermentation in cylinder were studied. On the basis of single factor test,an orthogonal test was carried out to find optimal conditions for laccase production P. chrysosporium through liquid fermentation. These results showed that the stirring speed of fermentation cylinder had the highest effect on laccase production,and the optimal conditions were shown as follows： the temperature at 28 ℃,the rotating speed at 300 r/min,the ventilation volume of 5 L/min（ ventilation ratio of 1.0 vvm）,the initial p H of medium of 5,and the inoculation amount of 15%,which gave the highest laccase level of 14. 86 U/ml.

Gas–solid reactor optimization based on EMMS-DPM simulation and machine learning

Design,scaling-up,and optimization of industrial reactors mainly depend on step-by-step experiments and engineering experience,which is usually time-consuming,high cost,and high risk.Although numerical simulation can reproduce high resolution details of hydrodynamics,thermal transfer,and reaction process in reactors,it is still challenging for industrial reactors due to huge computational cost.In this study,by combining the numerical simulation and artificial intelligence(AI)technology of machine learning(ML),a method is proposed to efficiently predict and optimize the performance of industrial reactors.A gas–solid fluidization reactor for the methanol to olefins process is taken as an example.1500 cases under different conditions are simulated by the coarse-grain discrete particle method based on the Energy-Minimization Multi-Scale model,and thus,the reactor performance data set is constructed.To develop an efficient reactor performance prediction model influenced by multiple factors,the ML method is established including the ensemble learning strategy and automatic hyperparameter optimization technique,which has better performance than the methods based on the artificial neural network.Furthermore,the operating conditions for highest yield of ethylene and propylene or lowest pressure drop are searched with the particle swarm optimization algorithm due to its strength to solve non-linear optimization problems.Results show that decreasing the methanol inflow rate and increasing the catalyst inventory can maximize the yield,while decreasing methanol the inflow rate and reducing the catalyst inventory can minimize the pressure drop.The two objectives are thus conflicting,and the practical operations need to be compromised under different circumstance.

Embodied energy consumption and carbon emissions evaluation for urban industrial structure optimization

Cities are the main material processors asso- ciated with industrialization. The development of urban production based on fossil fuels is the major contributor to the rise of greenhouse gas density, and to global warming. The concept of urban industrial structure optimization is considered to be a solution to urban sustainable develop- ment and global climate issues. Enforcing energy con- servation and reducing carbon emissions are playing key roles in addressing these issues. As such, quantitative accounting and the evaluation of energy consumption and corresponding carbon emissions, which are by-products of urban production, are critical, in order to discover potential opportunities to save energy and to reduce emissions. Conventional evaluation indicators, such as ＂energy consumption per unit output value＂ and ＂emissions per unit output value＂, are concerned with immediate consumptions and emissions; while the indirect consump- tions and emissions that occur throughout the supply chain are ignored. This does not support the optimization of the overall urban industrial system. To present a systematic evaluation framework for cities, this study constructs new evaluation indicators, based on the concepts of ＂embodied energy＂ and ＂embodied carbon emissions＂, which take both the immediate and indirect effects of energy consumption and emissions into account. Taking Beijing as a case, conventional evaluation indicators are compared with the newly constructed ones. Results show that the energy consumption and emissions of urban industries are represented better by the new indicators than by conventional indicators, and provide useful information for urban industrial structure optimization.

Impact of Artificial Intelligence on Economic Development

The economic growth pattern in China is changing from high-speed development to high-quality development.In order to promote high-quality economic development,it is necessary to promote the transformation and upgrading of all aspects of social reproduction.Artificial intelligence has promoted high-quality economic development in China from the aspects of innovation effect,technology spillover effect,factor improvement effect,and technology optimization effect.However,there are still several problems in China's Al development,such as the lack of core technologies,privacy and security,as well as the lack of high-tech talents.To this end,China should advance basic theoretical research and key generic technology development to improve the self-reliance of science and technology,as well as optimize the development environment of the industry and improve the relevant laws and regulations as well as the ethical norms system.A system to train high-quality personnel and a mechanism that allows personnel flow should be established to promote high-quality economic development.