Penicillin fermentation residue biochar as a high-performance electrode for membrane capacitive deionization

Membrane capacitive deionization(MCDI)is an efficient desalination technology for brine.Penicillin fermentation residue biochar(PFRB)possesses a hierarchical porous and O/N-doped structure which could serve as a high-capacity desalination electrode in the MCDI system.Under optimal conditions(electrode weight,voltage,and concentration)and a carbonization temperature of 700℃,the maximum salt adsorption capacity of the electrode can reach 26.4 mg/g,which is higher than that of most carbon electrodes.Furthermore,the electrochemical properties of the PFRB electrode were characterized through cyclic voltammetry(CV)and electrochemical impedance spectroscopy(EIS)with a maximum specific capacitance of 212.18 F/g.Finally,biotoxicity tests have showed that PFRB was non-biotoxin against luminescent bacteria and the MCDI system with the PFRB electrode remained stable even after 27 adsorption–desorption cycles.This study provides a novel way to recycle penicillin residue and an electrode that can achieve excellent desalination.

Model penicillin fermentation by least squares support vector machine with tuning based on amended harmony search

Penicillin fermentation is an important part of microbial fermentation. Due to the existence of error date in the independent variables and dependent variables of the penicillin fermentation sample data, the accuracy of the model of penicillin fermentation is affected. In this paper, an amended harmony search （AHS） algorithm is developed to adjust the hyper-parameters of least squares support vector machine （LS-SVM） in order to build penicillin fermentation process model with prediction accuracy. The AHS algorithm is investigated by unconstrained benchmark functions with different characteristics. Compared with other several optimization approaches, AHS demonstrates a better performance. Moreover, using the simulation data from the PenSim simulation platform to validate the effectiveness of the penicillin fermentation process modeling, experiment results show that the penicillin fermentation process modeling based on the tuned LS-SVM by AHS possesses robustness and generalization ability.

A cellular automata model for simulating fed-batch penicillin fermentation process

A cellular automata model to simulate penicillin fed-batch fermentation process(CAPFM)was established in this study,based on a morphologically structured dynamic penicillin production model,that is in turn based on the growth mechanism of penicillin producing microorganisms and the characteristics of penicillin fed-batch fermentation.CAPFM uses the three-dimensional cellular automata as a growth space,and a Moore-type neighborhood as the cellular neighborhood.The transition rules of CAPFM are designed based on mechanical and structural kinetic models of penicillin batch-fed fermentation processes.Every cell of CAPFM represents a single or specific number of penicillin producing microorganisms,and has various state.The simulation experimental results show that CAPFM replicates the evolutionary behavior of penicillin batch-fed fermentation processes described by the structured penicillin production kinetic model accordingly.

On-line Scheduling Algorithm for Penicillin Fed-batch Fermentation

An on-line scheduling algorithm to maximize gross profit of penicillin fed-batch fermentation is proposed. According to the on-line classification method, fed-batch fermentation batches are classified into three categories. Using the scheduling strategy, the optimal termination sequence of batches is obtained. Pseudo on-line simulations for testing the proposed algorithm with the data from industrial scale penicillin fermentation are carried out.

Monitoring and Fault Diagnosis for Batch Process Based on Feature Extract in Fisher Subspace

Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calcu- lated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or filling in the unknown portion of the process vari- ables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of peni- cillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.