The multi-scale structures of complex flows in chemical engineering have been great challenges to the design and scaling of such systems, and multi-scale modeling is the natural way in response. Particle methods (PMs) are ideal constituents and powerful tools of multi-scale models, owing to their physical fidelity and computational simplicity. Especially, pseudo-particle modeling (PPM, Ge & Li, 1996; Ge & Li, 2003) is most suitable for molecular scale flow prediction and exploration of the origin of multi-scale structures; macro-scale PPM (MaPPM, Ge & Li, 2001) and similar models are advantageous for meso-scale simulations of flows with complex and dynamic discontinuity, while the lattice Boltzmann model is more competent for homogeneous media in complex geometries; and meso-scale methods such as dissipative particle dynamics are unique tools for complex fluids of uncertain properties or flows with strong thermal fluctuations. All these methods are favorable for seamless interconnection of models for different scales. However, as PMs are not originally designed as either tools for complexity or constituents of multi-scale models, further improvements are expected. PPM is proposed for microscopic simulation of particle-fluid systems as a combination of molecular dynamics (MD) and direct simulation Monte-Carlo (DSMC). The collision dynamics in PPM is identical to that of hard-sphere MD, so that mass, momentum and energy are conserved to machine accuracy. However, the collision detection procedure, which is most time-consuming and difficult to be parallelized for hard-sphere MD, has been greatly simplified to a procedure identical to that of soft-sphere MD. Actually, the physical model behind such a treatment is essentially different from MD and is more similar to DSMC, but an intrinsic difference is that in DSMC the collisions follow designed statistical rules that are reflection of the real physical processes only in very limited cases such as dilute gas. PPM is ideal for exploring the mechanism of complex flows ab initio. In final analysis, the complexity of flow behavior is shaped by two components on the micro-scale: the relative displacements and interactions of the numerous molecules. Adding to the generality of the characteristics of complex system as described by Li and Kwauk (2003), we notice that complex structures or behaviors are most probably observed when these two components are competitive and hence they must compromise, as in the case of emulsions and the so-called soft-matter that includes most bio-systems. When either displacement or interaction is dominant, as in the case of dilute gas or solid crystals, respectively, complexity is much less spectacular. Most PMs consist explicitly of these two components, which is operator splitting in a numerical sense, but it is physically more meaningful and concise in PPM. The properties of the pseudo-particle fluid are in good conformance to typical simple gas (Ge et al., 2003; Ge et al., 2005). The ability of PPM to describe the dynamic transport process on the micro-scale in heterogeneous particle-fluid systems has been demonstrated in recent simulations (Ge et al., 2005). Especially, the method has been employed to study the temporal evolution of the stability criterion in the energy minimization multi-scale model (Li & Kwauk, 1994), which confirms its monotonously asymptotic decreasing as the model has assumed (Zhang et al., 2005). Massive parallel processing is also practiced for simulating particle-fluid systems in PPM, indicating an optimistic prospect to elevate the computational limitations on their wider applications, and exploring deeper underlying mechanism in complex particle-fluid systems. The multi-scale structures of complex flows in chemical engineering have been great challenges to the design and scaling of such systems, and multi-scale modeling is the natural way in response. Particle methods (PMs) are ideal constituents and powerful tools of multi- Especially, pseudo-particle modeling (PPM, Ge & Li, 1996; Ge & Li, 2003) is most suitable for molecular scale flow prediction and exploration of the origin of multi-scale structures; macro-scale PPM (MaPPM, Ge & Li, 2001) and similar models are advantageous for meso-scale simulations of flows with complex and dynamic discontinuity, while the lattice Boltzmann model is more competent for homogeneous media in complex geometries; and meso -scale methods such as dissipative particle dynamics are unique tools for complex fluids of uncertain properties or flows with strong thermal fluctuations. All these methods are favorable for seamless interconnects However, as of PMs are not originally designed as either tools for complexity or constituents of multi-scale models, further improvements are expected. PPM is proposed for microscopic simulation of particle-fluid systems as a combination of molecular dynamics (MD) and direct simulation Monte-Carlo (DSMC). The collision dynamics in PPM is identical to that of hard-sphere MD, so that mass, momentum and energy are conserved to machine accuracy. However, the collision detection procedure, which is most time-consuming and difficult to be parallelized for hard-sphere MD, has been simplified identical to a procedure identical to that of soft-sphere MD. Actually, the physical model behind such a treatment is essentially different from MD and is more similar to to DSMC, but an intrinsic difference is that in DSMC the collisions follow designed statistical rules that are reflection of the real physical processes only in very limited cases such as dilute gas. PPM is ideal for exploring the mechanism of complex flows ab initio. In final analysis, the complexity of flow behavior is shaped by two components on the micro-scale: the relative displacements and interactions of the numerous molecules. Adding to the generality of the characteristics of complex system as described by Li and Kwauk (2003), we notice that complex structures or behaviors are most probably observed when these two components are competitive and therefore they must compromise, as in the case of emulsions and the so-called soft-matter that includes Most bio-systems. When either displacement or interaction is dominant, as in the case of dilute gas or solid crystals, respectively, complexity is much less spectacular. Most PMs consist explicitly of these two components, which is operator splitting in a numerical sense, but it is physically more meaningful and concise in PPM. The properties of the pseudo-particle fluid are in good conformance to typical simple gas (Ge et al., 2003; Ge et al., 2005). The ability of PPM to describe the dynamic transport process on the micro-scale in heterogeneous particle-fluid systems has been demonstrated in recent simulations (Ge et al., 2005). Especially, the method has been employed to study the temporal evolution of the stability criterion in the energy minimization multi-scale model (Li & Kwauk, 1994), which confirms its monotonously asymptotic decreasing as the model has assumed (Zhang et al., 2005). Massive parallel processing is also practiced for simulating particle -fluid systems in PPM, indicating an optimistic prospect to elevate the computational limitations on their wider applications, and exploring lower underlying mechanisms in complex particle-fluid systems.