Efficient energy production from biomass is a central issue in the context of clean alternative energy resource. In this work we propose a novel model based on spiking neural networks cubes in order to model the chemical processes that goes on in a digestor for the production of usable biogas. For the implementation of the predictive structure, we have used the NeuCube computational framework. The goals of the proposed model were: develop a tool for real applications (low-cost and efficient), generalize the data when the system presents high sensitivity to small differences on the initial conditions, take in account the “multi-scale” temporal dynamics of the chemical processes occurring in the digestor, since the variations present in the early stages of the processes are very quick, whereas in the later stages are slower. By using the first ten days of observation the implemented system has been proven able to predict the evolution of the chemical process up to the 100th day obtaining a high degree of accuracy with respect to the experimental data measured in laboratory. This is due to the fact that the spiking neural networks have shown to be able to modeling complex information processes and then it has been shown that spiking neurons are able to handle patterns of activity that spans different time scales. Thanks to such properties, our system is able to capture the multi-scale trend of the time series associated to the early-stage evolutions, as well as their interaction, which are crucial in the point of view of the information content to obtain a good long-term prediction.

在清洁替代能源的背景下，利用生物质高效生产能源是一个中心问题。在这项工作中，我们提出了一种基于尖峰神经网络立方体的新颖模型，以便对在消化池中进行的生产可用沼气的化学过程进行建模。为了实现预测结构，我们使用了NeuCube计算框架。提出的模型的目标是：开发用于实际应用的工具（低成本高效），当系统对初始条件下的细微差异表现出高度敏感性时，对数据进行泛化，并考虑“多尺度”时间动态消化器中发生的化学过程的变化，因为在过程的早期阶段中存在的变化非常快，而在后期阶段中的变化则较慢。通过使用观察的前十天，已证明所实施的系统能够预测直到100天的化学过程的演变，从而获得相对于实验室测量的实验数据而言的高度准确性。这是由于事实，尖峰神经网络已经能够对复杂的信息过程进行建模，然后又表明尖峰神经元能够处理跨越不同时间尺度的活动模式。由于这些特性，我们的系统能够捕获与早期演化相关的时间序列的多尺度趋势以及它们之间的相互作用，这对于获得良好的信息内容至关重要。长期预测。