隨著人口的逐年增長，對食物和能源的需求也隨之增加，這將導致廢棄物和污染物的產生。因此，必須尋找替代能源以減少對環境的負面影響。目前，風能、水能、太陽能、生質能等許多可再生能源的應用持續增長。如今，生質物可以分為多種類型，包括農產品、固體廢棄物、工業廢物等。這些生質物材料可以通過生物化學或熱化學方法轉化為有用的生質燃料，例如沼氣、生質酒精、生質柴油等。這些方法不僅可以替代當前的能源，而且可以克服人口增長引起的能源危機。本研究也透過機器學習輔助實驗預測出生產葡萄糖的最佳濃度，有助於生質酒精的生產及應用。
第一部分旨在通過濕式焙燒及糖化（wet torrefaction, WT）高粱酒粕渣（sorghum distillery residue, SDR）來生產水焦炭和生質酒精。實驗根據反應曲面方法(response surface methodology, RSM)中的Box-Behnken設計，其中操作條件包括硫酸濃度（0、0.01和0.02 M）、澱粉葡萄糖苷酶濃度（36、51和66 IU）和糖化時間（120、180和240分鐘）。與常規乾式焙燒相比，水焦炭收率介於13.24 ％至14.73％之間，遠低於乾式焙燒生物炭（收率 > 50％）。原始高粱酒粕渣的熱值是17.15 MJ·kg-1，濕式焙燒後顯著提高到22.36-23.37 MJ·kg-1。當硫酸濃度從0 M增加到0.02 M時，產品中的葡萄糖濃度從5.59 g·L-1增加到13.05 g·L-1。方差分析的預測表明，最大葡萄糖濃度的最佳條件組合是使用濃度為0.02 M 的硫酸進行濕式焙燒後，在以66 IU濃度的酵素酶進行120分鐘的糖化，且葡萄糖濃度為30.85 g·L-1。在本研究中，獲得的最大生質酒精濃度為19.21 g·L-1，高於小麥秸稈（18.1 g·L-1）和甜高粱渣（16.2 g ·L-1）的生質酒精濃度。高粱酒生產過程中會產生大量的高粱酒粕渣，如果處理不當，可能會造成環境問題。這項研究實現高粱酒粕渣的增值，從而降低了環境污染，甚至實現了循環經濟。
本研究中的第二部分，將機器學習應用於葡萄糖濃度的預測。人工智能（AI）已成為未來的趨勢，其中將數據提供給機器學習，然後將AI用於預測。這項研究使用數據分析來優化實驗，以便找到最佳條件並獲得用於生產生質酒精的最大葡萄糖濃度。濕式烘焙（WT）用於進行生質酒精的預處理和預測最佳條件以獲得最大葡萄糖濃度。將數據按7：4的比例分為訓練和測試數據。使用神經網絡（NN）和多元適應性雲形迴歸（MARS），然後是決策樹（DT）對材料的分類進行預測，從而對來自五種不同原料的葡萄糖濃度進行了訓練和預測。 NN的預測結果優於MARS，因此將NN訓練用於BBD實驗的預測。通過Box-Behnken設計（BBD）實驗條件在反應曲面方法（RSM）中對SDR進行了測試，並在包括溫度（170、175和180 °C）的操作條件下對AI預測結果進行了最佳驗證。 ，反應時間（10、20和30分鐘）和硫酸（0、0.01和0.02 M）。由於未獲得良好的結果，因此將BBD實驗的結果添加到訓練中（總共66個數據集），得出R2=0.997。最終，在BBD和NN中確定了0.02 M硫酸濃度的最佳條件，分別在180 °C下30分鐘和173 °C下10.5分鐘。與BBD操作條件相比，由於NN預測的溫度較低且反應時間較短，因此NN模型具有更高的成本效益。總之，神經網絡是本研究中最合適的預測方法。還已經表明，利用先驗數據可以實現訓練和預測。

As the population grows every year, the demand for food and energy increases, which leads to the generation of waste and pollutants. Therefore, it is necessary to find alternative energy sources to reduce the negative impact on the environment. Currently, the use of wind energy, hydropower energy, solar energy, bioenergy, and many other renewable energy sources continues to grow. Today, biomass can be classified into various types, including agricultural products, solid waste, and industrial waste. These biomass materials can be biochemically or thermochemically converted into useful biofuels such as biogas, bioethanol, biodiesel, and so forth. These methods can not only replace the current energy sources but also overcome the energy crisis caused by population growth. This study also predicts the optimal concentration of glucose concentration through machine learning, which can help in the production and application of bioethanol.
The first part aimed to go through the wet torrefaction (WT) and saccharification of sorghum distillate residue (SDR) towards hydrochar and bioethanol production. The experiments are designed by Box-Behnken design from response surface methodology where the operating conditions include sulfuric acid concentration (0, 0.01, and 0.02 M), amyloglucosidase concentration (36, 51, and 66 IU), and saccharification time (120, 180, and 240 min). Compared to conventional dry torrefaction, the hydrochar yield is between 13.24 and 14.73%, which is much lower than dry torrefaction biochar (yield > 50%). The calorific value of the raw SDR is 17.15 MJ·kg-1, which is significantly enhanced to 22.36-23.37 MJ·kg-1 after WT. When the sulfuric acid concentration increases from 0 to 0.02 M, the glucose concentration in the product increases from 5.59 g·L-1to 13.05 g·L-1. The prediction of analysis of variance suggests that the best combination to maximum glucose production is 0.02 M H2SO4, 66 IU enzyme concentration, and 120 min saccharification time, and the glucose concentration is 30.85 g·L-1. The maximum bioethanol concentration of 19.21 g·L-1 is obtained, which is higher than those from wheat straw (18.1 g·L-1) and sweet sorghum residue (16.2 g·L-1). A large amount of SDR is generated in the kaoliang liquor production process, which may cause environmental problems if it is not appropriately treated. This study fulfills SDR valorization for hydrochar and bioenergy to lower environmental pollution and even achieve a circular economy.
For the second part, artificial intelligence (AI) has become the future trend to be used for prediction after the data is provided to machines learning. This study uses data analysis to optimize the experiment to find the best conditions and obtain the maximum concentration of glucose for the production of bioethanol. Wet torrefaction (WT) is used to perform the pretreatment of bioethanol and the prediction of the best conditions to obtain the maximum concentration of glucose. Forty-nine (49) sets of data are split into training and test data in the ratio of 7:4. Glucose concentrations from five different feedstocks are trained and predicted using a neural network (NN) and multivariate adaptive regression samples (MARS), followed by a decision tree (DT) to predict the classification of the materials. The predicted NN results are better than MARS, so the NN training is used for the prediction of the Box-Behnken design (BBD) experiment. The SDR is tested by the BBD experimental conditions in the response surface method (RSM), and the best verification of the AI prediction results is performed with operating conditions including temperature (170, 175, and 180 °C), reaction time (10, 20, and 30 min), and sulfuric acid concentration (0, 0.01, and 0.02 M). Since good results are not obtained, the results of the BBD experiment are added to the training (66 data sets in total), yielding 99.70% goodness of fit. Finally, the optimal conditions are determined in BBD and NN for 0.02 M sulfuric acid concentration at 180 °C for 30 min and at 173 °C for 10.5 min, respectively. Compared to the BBD operating conditions, the NN model is more cost-effective due to the lower temperature and shorter reaction time of the NN prediction. In conclusion, NN is the most suitable prediction method in this study. It has also been shown that training and prediction can be achieved with prior data.

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