Energy and Production Efficiency Optimization of an Ethylene Plant Considering Process Operation and Structure

Life Cycle Assessment and Multiobjective Optimization for Steam Cracking Process in Ethylene Plant

With energy savings and emission reduction becoming national policies in recent years, the environmental impacts of industrial production are more and more critical. Most of the studies have concentrated on the environmental effects of the industrial production process. Little attention has been paid to the energy consumption and pollution emission in extracting, processing, and transporting the feedstock and other secondary materials. An integrated multiobjective optimization framework is proposed for the steam cracking process on the basis of a life cycle assessment and data-driven modeling methods. A multiobjective economic-environmental optimization model is developed on the basis of industrial and simulated data. A multiobjective optimization model combined with energy cost is also developed for comparative study. The nondominated sorting genetic algorithm-II is utilized to solve the problems, and the Pareto front is obtained. An industrial case study is carried out to indicate the effectiveness of the proposed method. The results show that the LCA-based method can better represent the environmental impacts in comparison with the standard energy cost model. Therefore, the proposed method can achieve a better tradeoff between economic benefits and environmental impacts for guiding ethylene production.

Energy and Environmental Assessment of Steam Management Optimization in an Ethylene Plant

Steam crackers (ethylene plants) belong to the most complex industrial plants and offer significant potential for energy-saving translated into the reduction of greenhouse gas emissions. Steam export to or import from adjacent units or complexes can boost the associated financial benefit, but its energy and environmental impact are questionable. A study was carried out on a medium-capacity ethylene plant using field data to: 1. Estimate the energy savings potential achievable by optimizing internal steam management and optimizing steam export/import; 2. Quantify the associated change in air pollutant emissions; 3. Analyze the impact of the increasing carbon price on the measures adopted. Internal steam management optimization yielded steam let-down rate minimization and resulted in a 5% (87 TJ/year) reduction in steam cracker’s steam boiler fuel consumption and the associated cut of CO₂ emissions by almost 4900 t/year and that of NO_x emissions by more than 5 t/year. Steam import to the ethylene plant from the refinery proved to be purely economic-driven, as it increased the net fuel consumption of the ethylene plant and the refinery complex by 12 TJ/year and resulted in an increase of net emissions of nearly all considered air pollutants (more than 7000 t/year of CO₂, over 15 t/year of NO_x, over 18 t/year of SO_x) except for CO, where the net change was almost zero. The effect of external emissions change due to the associated backpressure electricity production surplus (over 11 GWh/year) was too low to compensate for this increase unless fossil fuel-based electricity production was considered. The increase of carbon price impact on the internal steam management optimization economics was favorable, while a switch to steam export from the ethylene plant, instead of steam import, might be feasible if the carbon price increased to over 100 €/tCO₂.

Optimization-assisted water supplement mechanism with energy efficiency in IoT based greenhouse

Remarkable resource management and energy efficiency improvements can be achieved in greenhouses using innovative technological advancements and modern agricultural methods. Deployment of Internet of Things (IoT) and optimization algorithms in greenhouse farming is highly desirable for real-time monitoring and controlling various parameters with optimal solutions. However, IoT based greenhouses require more energy as compared to traditional farming. This paper proposes an optimal greenhouse water supplement mechanism with efficient energy consumption based on IoT and optimization techniques. The first contribution of this study is to gather the actual water and soil moisture levels from the greenhouse and tank using IoT devices. Secondly, the formulation and deployment of an objective function to compute the optimal water and soil moisture levels for greenhouse and tank based on user-desired settings, the system constraints and actual sensing values. We applied a rule-based expert system to activate water pumps with the required flow rate and operational duration to achieve efficient energy consumption. To prove the effectiveness of the proposed concept, embedded IoT devices and objective function for optimization are deployed as well as, a number of experiments are conducted to provide the optimal water and soil moisture levels in a real greenhouse and water tank environment.

Novel manifold learning based virtual sample generation for optimizing soft sensor with small data

Due to the extremely complex mechanism and strong non-linear characteristics of industrial processes, data-driven soft sensor technologies play a key role in the intelligent measurement of process industries. However, the information of the collected process data in the steady stage is quite limited and unreliable, causing the small sample problem. As a result, it becomes an intractable challenge to catch the nature of the process and build accurate soft sensor models. To solve this problem, this paper proposes a novel manifold learning based virtual sample generation method (Isomap-VSG) to generate feasible virtual samples in the information gaps for supplementing the original small sample space. To find data sparse regions reasonably, one kind of manifold learning methods called Isomap is used to visualize process data with high dimension. Then virtual samples can be generated by the interpolation method and extreme learning machine. The simulation results on a standard dataset and a real-world application demonstrate that, compared with other advanced methods, the proposed Isomap-VSG method can achieve better performance in terms of generating feasible virtual samples and improving the accuracy of soft sensor models using limited samples.

A hybrid air pollutant concentration prediction model combining secondary decomposition and sequence reconstruction

Acid rain is a serious threat to terrestrial ecosystems. To provide more accurate early warning information for acid rain prevention, urban planning, and travel planning, a novel air pollutant prediction model was proposed in this paper to predict NO₂ and SO₂. First, the data were decomposed into several sub-sequences by a complete ensemble empirical mode decomposition with adaptive noise. Second, the subsequences are reconstructed by variational mode decomposition and sample entropy. Then, the new subsequences are predicted by the extreme learning machine combined with the whale optimization algorithm. The empirical analysis was carried out through 8 data sets. According to the experimental results, three main conclusions can be drawn. First, the proposed model in this paper has excellent prediction performance and robustness. In all the comparison experiments, the R² and RMSE of the proposed model are the best among all the models. Second, data preprocessing is very necessary. After adding the decomposition algorithm, the average improvement levels of R² and RMSE were 897.57% and 50.78%, respectively. Third, the re-decomposition of IMF1 is an effective method to improve prediction accuracy. After the re-decomposition of IMF1, R² can be improved by 13.64% on average on the original basis, and RMSE can be reduced by 31.99% on average. The results of this study can provide a valuable reference for the research of air pollutant prediction. In future work, the application of the proposed model in other air pollutants or other regions can be explored.