The object of the modelling is a co-current fixed bed gasifier with thermal input of 75 kW_th, located in Pirna (Germany), operated by TU Dresden. Two sets of experiments (experiments 1-4 from 2006 and experiments 5-9 from 2013) were performed to analyse the process behaviour and to develop the model. Experiments were performed to measure following process parameters that will be further used for modelling purpose: biomass mass flow rate (m_b), air volume flow rate (m_air), syngas temperature at the exit of the gasifier, syngas composition, pressure in the reactor and temperature of inlet air. All the data was recorded on a 30 seconds base. The length of an experiment depends on gasifier initial conditions. For example, if the reactor was pre-heated due to previous utilization and the initial temperature in the reactor was relatively high (experiment 4) the time to reach stationary syngas production regime was much shorter, compared to experiment 1 where this was not the case. The goal of the experiments was to have a stable syngas production for approximately 3 hours in which the syngas composition was measured. The measurement of syngas composition started when the outlet syngas temperature was above or around 250 °C.

Biomass wood chips, distributed from a local provider, are used as fuel in the gasification process. Biomass composition has been determined by means of ultimate fuel analysis on wet basis for experiments 1-4 at TU Dresden laboratory before the start of operation and considered as constant during operation. The lower heat value of the biomass is 17.473 MJ/kg, carbon content is 47.40%, hydrogen content is 5.63%, moisture content is 7.87%, ash content is 0.55% and the content of chlorine is 0.01%. Biomass composition for the experiments 5-8 has not been determined before the start of experiments. Biomass is first fed manually in a small storage room, located in front of the valves for biomass feeding control. When the gasification bed height goes under a certain threshold (set by the control system) an alarm is activated and the operator can manually open the valves. Once the valve opens, the whole amount of biomass from the storage room is fed into biomass shredder. The biomass is shredded and fed into gasification reactor. Air flow rate for gasification is controlled manually by setting the air valves opening and distributed by air pumps. Ash removal is also controlled manually by opening the ash valves. The facility scheme is presented in Figure 1. The list of sensors and other details regarding plant design and operation can be found in Mikulandric et al. [24].

Figure 1.

Co-current fixed bed biomass gasification facility operated by TU Dresden [24]

Measurements of fuel flow rate are presented in Figure 2 and air flow rate in Figure 3. As it can be seen from Figure 2 there is an obvious difference between experiments 1-4 (conducted in 2006) and experiments 5-8. Experiment 9 is not presented due to practical reasons (better visual comparison between 2 set of experiments) as it is relatively short and will be used only for validation of temperature model. In experiments 1-4 fuel flow rate is relatively constant and ranges between 50 and 150 kg/h while in experiments 5-8 (and 9) fuel flow rate is generally higher and usually ranges between 50 and 250 kg/h. Air flow rate in experiments 1-4 is slightly higher than in experiments 5-8. This change indicates a shift from enhanced complete fuel combustion regime (experiments 1-4) towards incomplete fuel combustion regime (experiments 5-8) which results in lower process temperatures in experiments 5-8. As fuel flow rate control system (related to bed height alarm system) has not been changed (the system is described in Mikulandric et al. [24]) this shift represents a change in operating conditions that can be due to changes in fuel quality, amount of ash sintering or due to some other unknown reason.

Figure 2.

Fuel flow rate for experiments 1-8 [24]

Figure 3.

Air flow rate for experiments 1-8 [24]

Syngas temperature and syngas composition of presented gasification system is predicted through sub-models that are defined with non-linear functions. They include current and past fuel and air flow rates together with previous values of the output (syngas temperature and H₂, CO and CH₄ content) itself. Each sub-model can be represented as a nonlinear time series with following equation [29]:

$y\left(t\right)=f\left[y\left(t-1\right),\dots ,y\left(t-d_y\right),u\left(t-1\right),\dots ,u\left(t-d_u\right)\right]+e\left(t\right)$ (1)

where y(t) represents model output for the time t, u(t) model input for the time t, d_y, d_u corresponding number of lags (delays) for output and input, and e(t) error or noise for time t. For a detailed explanation of NARX structure authors refer to the research done by Chen and Billings [28]. By interaction of different non-linear sub-models (which could represent sub-processes during gasification) an effect of different input parameters on final gasification variables (syngas temperature and composition) can be defined.

For prediction of syngas temperature and quality a NARX model that consists of 2 layer network with 2-delay feedback with one hidden layer of 5 neurons has been proposed. Tan-sigmoid transfer function is used between hidden layers (as it provides a good trade-off between the calculation speed of sigmoid functions and prediction flexibility of tanh functions) and linear transfer function for output layer. After changing the number of training epochs to define the case with the best prediction quality it has been concluded that 600 training epochs provides the best prediction quality for considered system. More training epochs could lead to model overfitting. Fuel and air flow rates have been chosen as model inputs while syngas temperature is chosen as model output. Simplified model scheme is presented in Figure 4.

Figure 4.

General scheme of NARX temperature prediction model (for model training)

To analyse the effect of training data quantity on prediction performance, 10 different cases with different training data quantities have been defined (Table 1). For example in CASE 1, first 60 minutes have been used as training data for NARX model. The rest of the process (second part of experiment 1 and experiments 2-9) has been used for model validation (validation set of data). For model validation, syngas temperature and composition were predicted based on developed model and measured model inputs. In CASE 2 the data from the first 120 minutes of the process has been used for model training and the rest has been used for validation. In CASE 3 data from whole first experiment (first 800 minutes of the process) has been used for NARX training. Experiments 2-9 were used for model validation and prediction potential analysis. Later on (cases 4-10), the number of training data was increased until the data from experiments 1-8 was used as training data and only experiment 9 was used for model validation.

Furthermore, the number of model input delays has been varied from 1 to 20 in order to investigate the influence of model delays on temperature prediction performance. Model input delays are used during model training procedure as process history/memory data (feedback temperature loop and corresponding air and fuel flows for that moment). Higher number of model input delays means that during the training procedure more history data will be taken in consideration for prediction of future values. Each simulation delay represents an actual time delay of 30 seconds. Therefore, time delays for model inputs will range from 30 s to 10 minutes.

Continuous model prediction error will be analysed using eq. (2) and eq. (3) for syngas temperature (T) and volume fractions of constitutive gases (φ) while overall model prediction performance will be defined by using coefficient of determination (R²). APE represents time averaged absolute values of prediction errors.

$\text{error}\left(T\right)=\frac{T_{\text{predicted}}-T_{\text{measured}}}{T_{\text{measured}}}$ (2)

$\text{error}\left(\text{syngas composition}\right)=\frac{{\phi }_{\text{predicted}}-{\phi }_{\text{measured}}}{{\phi }_{\text{measured}}}$ (3)

Model prediction performance and model validation has been performed based on methods described in previous sections. First, a different size of training data sets has been used to analyse the influence of training data set size on prediction performance. Number of delays has been set to 2. Afterwards, the number of delays for model input has been varied in order to analyse the effect of model delays on prediction performance.

In the first case (Figure 5) first 60 minutes of experiment 1 have been used as training data set for NARX model. The rest of the process (experiment 1 from 60^th till 800^th minute and experiments 2-9) has been predicted based on developed NARX model (blue line) and measured model inputs. Simulation results show that the first 60 minutes (training data) of the experiment 1 has been described with very low prediction error (between ±10%). For comparison, simulation prediction error of commercial state-of-art software like ASPEN-PLUS on a different set of data and for fluidised bed reactor type is in the range of ±30% [30]. This is understandable because this data set was used as training data for model development. However, the rest of the process (part of the process data not used for the training) has not been described in a quality way. The prediction error that is well above 50% suggests that used training data size is generally not sufficient for modelling purpose. Negative prediction error values suggest that the syngas temperature is underestimated while positive prediction error values suggest that the syngas temperature has been overestimated. Resulting coefficient of determination (R²) has been defined at the end of performed simulation (presented in Table 1, CASE 1).

Due to a high prediction error from the first simulation case the training data set has been increased. In CASE 3 data from the whole experiment 1 has been used as training data set and syngas temperature from experiments 2-9 was predicted based on developed model and model inputs (experiments 2-9 are validation data). Simulation results show that for this training data set (experiment 1) model prediction error is usually below ±4%. For experiments 2-4 which are based on the same operating conditions but were not used for model training model prediction error is below ±8%. After changes in operating conditions (experiment 5-9) the prediction error generally rises but remains under ±10%. This general increase in model prediction error for experiments 5-9 is due to changes in operating conditions which current NARX model structure is not able to describe in a very precise way. Those changes in operating conditions could be due to use of different biomass quality or due to changes in the reactor (ash sintering). Use of different biomass compositions (moisture content or lower heat value) results in different syngas compositions but also in different temperature distributions in the reactor. Ash sintering could change the heat transfer between the reactor and environment which will result in different temperature distribution and consequently in different syngas composition. However, a prediction error under ±10% suggests that training data set from experiment 1 is still sufficient for general NARX model. Model performance for NARX model with experiment 1 as training data set is presented in Figure 6.

Figure 5.

Model performance with 60 minutes of training data set (CASE 1)

Figure 6.

Model performance with experiment 1 as training data set (CASE 3)

Different training data sizes have been used to analyse model prediction performance. Summary of the analysis is presented in Table 1. For example, in CASE 1 first 60 minutes of experiment 1 was used for model training. In CASE 2 first 120 minutes of experiment 1 was used for model training. In CASE 4, data from experiment 1 and 2 was used for model training. In CASE 10 data from experiment 1-8 was used for model training and experiment 9 was used for validation purpose. First 60 minutes as a data set for model training is not sufficient to develop a NARX model with reasonable prediction accuracy (prediction error under 30%). Average prediction error is above 40% and R² is 0.9. With increasing training data size the model prediction performance improves. However, with increase of a training data size beyond data from experiment 1 the model prediction performance does not increase significantly and it some cases it even declines. This leads to conclusion that increasing data size (after including data from experiment 1) leads to over-fitting of the model and does not contribute to increase of model prediction accuracy.

After the size of model training data has been determined (the whole experiment 1 has been used as training data set) a different number of model input delays and model output feedback delays have been used to analyse model prediction performance. With 2 delays of input and output variables (which represents a time delay of 1.5 minutes) the NARX model has the highest prediction performance. With increasing the number of delays prediction performance of temperature prediction model decreases. This can be due to a slow response of the model with a high number of delays. In the case of large number of delays a parameter history that is no longer relevant to the process is taken into consideration to predict future values. Furthermore, the number of delays gives the indication of particle residence time in the reactor. It implies that the residence time of particles close to oxidation zone is around 90 seconds. The summary of model prediction performance for different number of time delays is presented in Table 2.

The overall training and prediction time of developed NARX model for experiments 1-8 is 16 seconds which represents an adequate speed for on-line parameter prediction models. Together with model R² of 0.98 it can be concluded that developed NARX model can be used to predict syngas temperature in changeable operating conditions.

A similar modelling method has been used to predict volumetric content of H₂, CO and CH₄ in syngas. Model prediction performance for syngas composition predictions is presented in Figure 7 (H₂), Figure 8 (CH₄) and Figure 9 (CO). For prediction of syngas composition, the training dataset derived from experiment 1 was not of sufficient to quality describe the process. It must be emphasized that dataset of syngas composition is smaller than a dataset for process temperature as it was measured when temperatures reached 250 °C. Therefore, the training dataset had to be expanded to datasets from experiment 1 and 2 for H₂ and CH₄ values and datasets from experiments 1, 2 and 3 for CO values. In general, syngas composition predictions follow measured values with a good accuracy, with R² of prediction above 0.73 in all cases. The highest prediction error occurs in experiment 7 and during some parts in experiment 8 and 9.

Figure 7.

NARX model prediction performance for H₂ predictions

Figure 8.

NARX model prediction performance for CH₄ prediction

Figure 9.

NARX model prediction performance for CO predictions

The prediction performance of NARX models is summarised in Table 3. From the table it can be seen that the maximum temperature prediction deviation is 13.52 °C while the average temperature prediction deviation is 1.21 °C. For syngas composition in some sporadic time periods the model is not able to predict syngas composition values (example – Figure 7, H₂ prediction, experiment 8, 140-145 s). However, the average prediction deviation for syngas composition is below 1% vol. Resulting R² is 0.98 for temperature, 0.82 for CH₄, 0.73 for H₂ and 0.97 for CO.

The overall model prediction performance of developed models is presented in Table 4. NARX model performance has been compared with dynamic Adaptive Neuro Fuzzy Inference System (ANFIS) model, described and reported in Mikulandric et al. [24] and standard ANFIS model developed, described and reported in Mikulandric et al. [18] as the model training was performed on the same set of data. Compared to the training of NARX model the training of ANFIS model requires more data pre-processing and the feedback history of model input is very limited (only one delay is implemented). It can be seen that NARX model requires a significant smaller training database for a higher prediction performance. This also results in a faster prediction speed. The highest improvement can be seen in prediction of syngas composition quality where R² of NARX model ranges between 0.73 and 0.97 while R² of dynamic ANFIS model ranges between 0.45 and 0.83. Compared to standard ANFIS model, NARX model has a much better prediction performance for syngas temperature (1% of prediction error of NARX model compared to 7% of prediction error of standard ANFIS). Furthermore, standard ANFIS model is proven not to be appropriate as a simulation tool in changing operating conditions [24]. It has been concluded that NARX model shows a better model prediction performance than developed dynamic and standard ANFIS models. However, it should be noticed that in this kind of a comparison NARX model uses history of measured output data (temperature and syngas composition) in order to predict their future values. This means that NARX model should be constantly updated with measured past values of syngas temperatures. By this, prediction horizon of NARX model without active temperature measurements is quite limited. To compare performance indicators of NARX and dynamic ANFIS model for a longer-term predictions without active temperature measurements and history updates NARX model outputs/predictions were taken to update model output history (instead of measured temperatures).

Prediction potential of NARX model for a long-term temperature prediction is presented in Figure 10. By term ‘long-term’ is considered a time period between plant start-up and the point when the stationary operating conditions have been reached. It usually takes around 100 to 300 minutes for the plant to reach stationary regime from the start-up (Figure 6). For model performance analysis, 175^th minute of experiment 1 has been chosen as a starting point for the analysis. This point represents a middle point between process start-up and a moment where stationary operating condition has been achieved. Therefore, it was tested if the model can predict future values (in the time-span of 10 minutes) from 175^th minute of experiment 1 without using measurements as model input. It can be seen that NARX model cannot predict future temperature values in a quality way if it uses history of its own output as an input. In the first 3 minutes NARX model has history (2 delays) that equals to measured values. Based on that history the model can produce prediction with a relatively small prediction error. However, when algorithms start to use output of NARX model as history (in 4^th minute) the model soon becomes unstable and the prediction error reaches 1% in 9^th minute (5^th minute after the NARX model is disconnected from measurements). This is due to accumulation of prediction error that occurs in NARX model predictions. In the 3^rd minute (3^rd minute of real time represents 5^th minute of history) model history suggests that predicted temperature from NARX model is higher than measured one. Based on such suggestion and measured fuel and air flow rate the model decides to decrease predicted temperature. However, in the next time increment, the model history suggests that this value is too low (based on previous temperature and fuel and air flow rates) which results in a significant temperature prediction increase. In this way the model soon becomes unstable. A similar case was observed for syngas composition predictions.

Figure 10.

NARX model (temperature) prediction performance for second validation case

It can be concluded that NARX model can produce quality parameter prediction if measured values are used as history for model input. However, if model predictions are used as history for model input (like in long-term predictions) the model becomes unstable and produces high prediction errors. This leads to the conclusion that NARX models are very useful tool for a short term predictions (up to 5 minutes) and, therefore, could be used for short-term control loops. However, if such model is decoupled from real time measurements they can produce a significant prediction error. In comparison with dynamic ANFIS model which works on similar principle (if the prediction error is too high after some time it resets predicted value to measured one) NARX models seem to have a much lower autonomy in process prediction. Summarised comparison between developed NARX model, dynamic ANFIS model from Mikulandric et al. [24] and standard ANFIS model [18] for prediction of syngas temperature is presented in Table 5.