{'fraction_non_alphanumeric': 0.01898047722342733, 'fraction_numerical': 0.004338394793926247, 'mean_word_length': 5.318493150684931, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Optimisation and Control of Fed-Batch Production using Q-Learning

Industrial fed-batch processes are nonlinear dynamic process that requires good control and monitoring technique for multiobjective optimisation. For such control system, the complex nature of the process reactions that will affect the system stability and product concentrations have to be considered thoroughly. Normally, fed-batch production using open loop control through predetermined substrate feeding strongly relies on human operator’s experience to deal with the process uncertainties. This paper aims to determine the optimal input flow rate of a fed-batch process using a Q-learning (QL) algorithm to maximise the desired products while minimise the undesired products simultaneously. The main purpose of QL is to reduce the dependency on human expertise in the fed-batch process control. QL is attempted for its learning ability that builds up experience of the process by interacting with the process environment, which is then used to seek for the optimal substrate or input profile through iterative reward calculations. The performance of QL-suggested feeding profile was compared to the predetermined exponential feeding (EF) profile. Multistep action (MSA) has been implemented in consideration of the inborn process delay for some substrate feeding to take effect or react. From the results, QL is able to enhance the final desired production of the system to 20.86% whereas the explorative action of QL improves the feeding profile by 24.98%. The total undesired product using QL is less than EF, at a negligible concentration. QL is therefore a considerable alternative to perform multiobjective optimisation in a dynamic and frequently changing process environment and is able to suggest optimal substrate feeding profile that satisfies the process goal.